{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "归问题的典型性能衡量指标是均方根误差（RMSE） ， 它测量的是预测过程中， 预测错误的标准偏差。\n",
    "\n",
    "$$ RMSE(X, h)=\\sqrt{\\frac{1}{m}\\sum^m_{i=1}(h(x^{(i)})-y^{i})^2} $$\n",
    "- m是在测量RMSE时， 所使用的数据集中实例的数量\n",
    "- X（i） 是数据集中， 第i个实例的所有特征值的向量（标签特征除外） ， y（i） 是标签（也就是我们期待该实例的输出值） 。\n",
    "- h是系统的预测函数， 也称为一个假设。 当给定系统一个实例的特征向量x（i） ， 它会输出一个预测值 （ 读作“y-hat”）\n",
    "- RMSE（X， h） 是使用假设h在示例上测量的成本函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当有很多离群区域时， 你可以考虑使用平均绝对误差（也称为平均绝对偏差）\n",
    "$$ MAE(X,h)=\\frac{1}{m}\\sum^m_{i=1}|h(x^{(i)})-y^{(i)}| $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 计算平方和的根（RMSE） 对应欧几里得范数： 它应该是你比较熟悉的距离概念。 也称之为 范数， 记作||·||2（或者||·||） \n",
    "- 计算绝对值的总和（MAE） 对应 范数， 记作||·||1。 有时它也被称为曼哈顿距离， 因为它在测量一个城市的两点之间的距离时， 只能沿着正交的城市街区行走。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_hosing_data():\n",
    "    return pd.read_csv('datasets/housing/housing.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 快速查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_hosing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AvwncCXwBeFmptgq4otzeVO5Ttn8+M7OUn17O3nYksBS4vj+tkKR6czqbJEmSpDo4HNhY1kXaB7g8Mz8bEXcAl0bE24AvAxeX+hcDH42ILVQjkE4HyMzbI+Jy4A5gN3B2mSYnSWrDJJIkSZKkoZeZXwWe36T8HpqcXS0zfwSc1mJfFwAXdDtGSRp1TmeTJEmSJElSWyaRJEmSJEmS1JZJJEmSJEmSJLVlEkmSJEmSJEltmUSSJEmSJElSWyaRJEk9FxEbIuKBiLitoex/RsTXIuKrEfGZiFjYsO3ciNgSEXdFxIkN5StK2ZaIWNvvdkiSJEnzmUkkSVI/fARYMa1sM/DczPwV4OvAuQARcRRwOvCc8pj3R8SCiFgAvA84CTgKeGWpK0mSJKkPTCJJknouM78I7JxW9rnM3F3uXgssLrdXApdm5o8z815gC3BMuWzJzHsy8yfApaWuJEmSpD7Yd9ABSJIEvBa4rNxeRJVUmrKtlAHcN6382GY7i4jVwGqAsbExJiYmZnzyycnJtnXqapTbBqPdPttWT63atmbZ7j3KRvVvIEkaXSaRJEkDFRH/DdgNfGyqqEm1pPno2Wy2z8xcD6wHWL58eY6Pj88Yw8TEBO3q1NUotw1Gu322rZ5ate3MtVfuUbb1jD3rSZI0zEwiSZIGJiJWAS8Fjs/MqYTQNuCIhmqLge3ldqtySZIkST3mmkiSpIGIiBXAm4DfzsxHGjZtAk6PiP0j4khgKXA9cAOwNCKOjIj9qBbf3tTvuCVJkqT5ypFIkqSei4hPAOPAoRGxDTiP6mxs+wObIwLg2sz8g8y8PSIuB+6gmuZ2dmb+tOznHOBqYAGwITNv73tjJEmSpHnKJJIkqecy85VNii+eof4FwAVNyq8CrupiaJIkSZJmyelskiRJkiRJasskkiRJkiRJktoyiSRJkiRJkqS2TCJJkiRJkiSpLRfWbmHJ2iublm9dd0qfI5EkSZIkSRo8RyJJkiRJkiSpLZNIkiRJkiRJasskkiRJkiRJktoyiSRJkiRJkqS2Zp1EiogFEfHliPhsuX9kRFwXEXdHxGURsV8p37/c31K2L2nYx7ml/K6IOLHbjZEkSZIkSVJvdDIS6fXAnQ333w5cmJlLgV3AWaX8LGBXZj4LuLDUIyKOAk4HngOsAN4fEQv2LnxJkiRJkiT1w6ySSBGxGDgF+FC5H8CLgU+WKhuBU8vtleU+Zfvxpf5K4NLM/HFm3gtsAY7pRiMkSZIkSZLUW/vOst67gDcCB5b7hwAPZubucn8bsKjcXgTcB5CZuyPioVJ/EXBtwz4bH/OoiFgNrAYYGxtjYmJitm151OTkZEePW7Nsd/tKxVzi6USnsQ+LusYN9Y3duPuvzrFLklR3EXEEcAnwdOBnwPrMfHdEvAX4feA7peqbM/Oq8phzqWZK/BT4k8y8upSvAN4NLAA+lJnr+tkWSaqrtkmkiHgp8EBm3hQR41PFTapmm20zPeaxgsz1wHqA5cuX5/j4+PQqbU1MTNDJ485ce+Ws6249o/N4OtFp7MOirnFDfWM37v6rc+ySJI2A3cCazLw5Ig4EboqIzWXbhZn5t42Vpy2n8QzgXyLiF8vm9wEvoTqwfUNEbMrMO/rSCkmqsdmMRHoR8NsRcTLwRODnqEYmLYyIfctopMXA9lJ/G3AEsC0i9gWeCuxsKJ/S+BhJkiRJaikzdwA7yu0fRMSdNJnZ0ODR5TSAeyOicTmNLZl5D0BEXFrqmkSSpDbaJpEy81zgXIAyEunPM/OMiPgH4GXApcAq4IrykE3l/r+W7Z/PzIyITcDHI+KdVEcClgLXd7c5kiRJkkZdOQP084HrqA56nxMRrwZupBqttIuZl9O4b1r5sU2eY6+X2Rh205f1GDug9VIfo9j++bZUge0dbf1q72zXRGrmTcClEfE24MvAxaX8YuCjJdO/k2oIKZl5e0RcTpXh3w2cnZk/3YvnlyRJkjTPRMRTgE8Bb8jM70fERcD5VEtlnA+8A3gtrZfTaHZyoZ4sszHspi/rsWbZbt5xa/OfiL1e1mMQ5ttSBbZ3tPWrvR0lkTJzApgot++hydnVMvNHwGktHn8BcEGnQUqSJElSRDyBKoH0scz8NEBm3t+w/YPAZ8vdmZbTcJkNSZqDZll4SZIkSRoqERFUsx7uzMx3NpQf3lDtd4Dbyu1NwOkRsX9EHMljy2ncACyNiCMjYj+qmROb+tEGSaq7vZnOJkmSJEn98iLgVcCtEXFLKXsz8MqIOJpqStpW4L/AzMtpRMQ5wNXAAmBDZt7ez4ZIUl2ZRJIkSZI09DLzSzRf5+iqGR7TdDmNzLxqpsdJkppzOpskSZIkSZLaMokkSZIkSZKktkwiSZJ6LiI2RMQDEXFbQ9nBEbE5Iu4u1weV8oiI90TEloj4akS8oOExq0r9uyNi1SDaIkmSJM1XJpEkSf3wEWDFtLK1wDWZuRS4ptwHOInqDDpLgdXARVAlnYDzgGOBY4DzphJPkiRJknrPJJIkqecy84vAzmnFK4GN5fZG4NSG8kuyci2wsJy++URgc2buzMxdwGb2TExJkiRJ6hGTSJKkQRnLzB0A5fqwUr4IuK+h3rZS1qpckiRJUh/sO+gAJEmaptnpm3OG8j13ELGaaiocY2NjTExMzPiEk5OTbevU1Si3DUa7fbatnlq1bc2y3XuUjerfQJI0ukwiSZIG5f6IODwzd5Tpag+U8m3AEQ31FgPbS/n4tPKJZjvOzPXAeoDly5fn+Ph4s2qPmpiYoF2duhrltsFot8+21VOrtp259so9yraesWc9SZKGmdPZJEmDsgmYOsPaKuCKhvJXl7O0HQc8VKa7XQ2cEBEHlQW1TyhlkiRJkvrAkUiSpJ6LiE9QjSI6NCK2UZ1lbR1weUScBXwTOK1Uvwo4GdgCPAK8BiAzd0bE+cANpd5bM3P6Yt2SJEmSesQkkiSp5zLzlS02Hd+kbgJnt9jPBmBDF0OTJEmSNEtOZ5MkSZIkSVJbJpEkSZIkSZLUlkkkSZIkSZIktWUSSZIkSZIkSW2ZRJIkSZIkSVJbJpEkSZIkSZLUlkkkSZIkSZIktWUSSZIkSZIkSW2ZRJIkSZIkSVJbJpEkSZIkSZLUlkkkSZIkSZIktWUSSZIkSZIkSW2ZRJIkSZIkSVJbJpEkSZIkSZLUlkkkSZIkSZIktWUSSZIkSdLQi4gjIuILEXFnRNweEa8v5QdHxOaIuLtcH1TKIyLeExFbIuKrEfGChn2tKvXvjohVg2qTJNWNSSRJkiRJdbAbWJOZzwaOA86OiKOAtcA1mbkUuKbcBzgJWFouq4GLoEo6AecBxwLHAOdNJZ4kSTPbd9ABSJIkabgsWXtl0/Kt607pcyTSYzJzB7Cj3P5BRNwJLAJWAuOl2kZgAnhTKb8kMxO4NiIWRsThpe7mzNwJEBGbgRXAJ/rWGEmqKZNIkiRJkmolIpYAzweuA8ZKgonM3BERh5Vqi4D7Gh62rZS1Kp/+HKupRjAxNjbGxMREV9swDNYs2/24+2MH7Fk2ZRTbPzk5OZLtasX2jrZ+tdckkiRJkqTaiIinAJ8C3pCZ34+IllWblOUM5Y8vyFwPrAdYvnx5jo+PzyneYXbmtFGHa5bt5h23Nv+JuPWM8T5E1F8TExOM4uvaiu0dbf1qr2siSZIkSaqFiHgCVQLpY5n56VJ8f5mmRrl+oJRvA45oePhiYPsM5ZKkNtomkSLiiRFxfUR8pZwF4S9L+ZERcV05o8FlEbFfKd+/3N9Sti9p2Ne5pfyuiDixV42SJEmSNFqiGnJ0MXBnZr6zYdMmYOoMa6uAKxrKX13O0nYc8FCZ9nY1cEJEHFQW1D6hlEmS2pjNSKQfAy/OzOcBRwMryofw24ELy1kQdgFnlfpnAbsy81nAhaUe5cwJpwPPoVq47v0RsaCbjZEkSZI0sl4EvAp4cUTcUi4nA+uAl0TE3cBLyn2Aq4B7gC3AB4E/AigLap8P3FAub51aZFuSNLO2ayKVsxlMlrtPKJcEXgz8binfCLyF6rSZK8ttgE8C7y1HDVYCl2bmj4F7I2IL1Sk1/7UbDZEkSVLnWp2JTRo2mfklmq9nBHB8k/oJnN1iXxuADd2LTpLmh1mtiRQRCyLiFqr5xZuBbwAPZubU0v2NZzR49GwHZftDwCHM8iwIkqT5JSL+tEyXvi0iPlGmUXc8ZVqSJElSb83q7GyZ+VPg6IhYCHwGeHazauV6r86C0I1TaXZ6artWp7FsptenzKvraQjrGjfUN3bj7r86xz6sImIR8CfAUZn5w4i4nGrq88lUU6YvjYgPUE2VvoiGKdMRcTrVlOlXDCh8SZIkaV6ZVRJpSmY+GBETwHHAwojYt4w2ajyjwdTZDrZFxL7AU4GdzPIsCN04lWanp7abfmrLmfT61JZ1PQ1hXeOG+sZu3P1X59iH3L7AARHx78CTgB10OGW6TFmQJEmS1ENtk0gR8TTg30sC6QDgN6mO/H4BeBlwKXueBWEV1VpHLwM+n5kZEZuAj0fEO4FnAEuB67vcHklSjWTmtyLib4FvAj8EPgfcxCynTEfE1JTp7zbut9NRraM8ymyU2waj3b5+tW0QI7Ln4+vW7O88qn8DSdLoms1IpMOBjeVMavsAl2fmZyPiDuDSiHgb8GWq021Srj9aFs7eSTUtgcy8vUxTuAPYDZxdpslJkuapcmrllcCRwIPAPwAnNanabsr04ws6HNU6yqPMRrltMNrt61fbBjEiez6+bs3+zr0e4S5JUrfN5uxsXwWe36T8Hqqzq00v/xFwWot9XQBc0HmYkqQR9ZvAvZn5HYCI+DTwQjqfMi1JkiSpx2Z1djZJknrkm8BxEfGkiAiqUzTfwWNTpqH5lGlomDLdx3glSZKkecskkiRpYDLzOqoFsm8GbqXql9YDbwL+rEyNPoTHT5k+pJT/GbC270FLkiRJ81RHZ2eTJKnbMvM84LxpxR1PmdbwWNJijZ2t607pcySSJEnqJkciSZIkSZIkqa15NRKp1ZFRSZIkSRpV/g6S1C2ORJIkSZIkSVJb82okUje4zoMkSZIkSZqPHIkkSZIkSZKktkwiSZIkSZIkqS2TSJIkSZIkSWrLJJIkSZIkSZLaMokkSZIkSZKktkwiSZIkSZIkqS2TSJIkSZIkSWpr30EHIEmS5ocla69sWr513Sl9jkSSJElzYRJJkiRpHmiVxJMkSZotp7NJkiRJkiSpLZNIkiRJkiRJasvpbJIkSZIk1VCzqcquNaheciSSJEmSJEmS2jKJJEmSJGnoRcSGiHggIm5rKHtLRHwrIm4pl5Mbtp0bEVsi4q6IOLGhfEUp2xIRa/vdDkmqM5NIkiRJkurgI8CKJuUXZubR5XIVQEQcBZwOPKc85v0RsSAiFgDvA04CjgJeWepKkmbBNZEkSZIkDb3M/GJELJll9ZXApZn5Y+DeiNgCHFO2bcnMewAi4tJS944uhytJI8kkkiRJkqQ6OyciXg3cCKzJzF3AIuDahjrbShnAfdPKj22204hYDawGGBsbY2Jiosth98+aZbtnVW/sgNZ169z+ViYnJ2vfrmavV6s2jUJ7O2F7e8MkkiRJkqS6ugg4H8hy/Q7gtUA0qZs0X84jm+04M9cD6wGWL1+e4+PjXQh3MM5scgavZtYs2807bm3+E3HrGeNdjGg4TExMUOfXFZq/tq1eq1Fobydsb2+YRJIkSZJUS5l5/9TtiPgg8NlydxtwREPVxcD2crtVuSSpDRfWliRJklRLEXF4w93fAabO3LYJOD0i9o+II4GlwPXADcDSiDgyIvajWnx7Uz9jlqQ6cySSJGmgImIh8CHguVRTCl4L3AVcBiwBtgIvz8xdERHAu4GTgUeAMzPz5gGErXluSbPpA+tOGUAke2oWmzQKIuITwDhwaERsA84DxiPiaKr+YyvwXwAy8/aIuJxqwezdwNmZ+dOyn3OAq4EFwIbMvL3PTZGk2jKJJEkatHcD/5yZLytHhZ8EvBm4JjPXRcRaYC3wJqpTMi8tl2Op1sJouiCqJGm0ZOYrmxRfPEP9C4ALmpRfBVzVxdAkad5wOpskaWAi4ueAX6f8CMjMn2Tmg1SnW95Yqm0ETi23VwKXZOVaYOG0qYvVaHYAACAASURBVAySJEmSesSRSJKkQXom8B3gwxHxPOAm4PXAWGbuAMjMHRFxWKm/iD1PzbwI2NG4005PyzzKp4AdRNtmeyrpKXsT36Beu05OqTxXc21bp3//TnSrjfPxf64f7xlJknrNJJIkaZD2BV4A/HFmXhcR76aautZKq1M2P76gw9Myj/IpYAfRttmeSnrK3pw2elCvXSenVJ6rubat079/J7rVxvn4P9eP94wkSb3mdDZJ0iBtA7Zl5nXl/iepkkr3T01TK9cPNNT31MySJEnSAJhEkiQNTGZ+G7gvIn6pFB1PdSadTcCqUrYKuKLc3gS8OirHAQ9NTXuTJEmS1FtOZ5MkDdofAx8rZ2a7B3gN1UGOyyPiLOCbwGml7lXAycAW4JFSV5IkSVIfmESSJA1UZt4CLG+y6fgmdRM4u+dBqa+WNFsrZt0pA4hkT81ikyRJmq/aJpEi4gjgEuDpwM+A9Zn57og4GLgMWAJsBV6embsiIoB3Ux0pfgQ4MzNvLvtaBfxF2fXbMnMjkiRJmhOTXJIkqZ9msybSbmBNZj4bOA44OyKOojp7zjWZuRS4hsfOpnMSsLRcVgMXAZSk03nAscAxwHkRcVAX2yJJkiRJkqQeaZtEyswdUyOJMvMHwJ3AImAlMDWSaCNwarm9ErgkK9cCC8uZdU4ENmfmzszcBWwGVnS1NZIkSZIkSeqJjtZEioglwPOB64CxqTPiZOaOiDisVFsE3NfwsG2lrFX59OdYTTWCibGxMSYmJjoJEYDJycmmj1uzbHfH+5qtucTZTKvYh11d44b6xm7c/Vfn2CVJkiRpb806iRQRTwE+BbwhM79fLX3UvGqTspyh/PEFmeuB9QDLly/P8fHx2Yb4qImJCZo97swerhuw9Yw9n28uWsU+7OoaN9Q3duPuvzrHLkndMMyLoEuSpN6bzZpIRMQTqBJIH8vMT5fi+8s0Ncr1A6V8G3BEw8MXA9tnKJckSZIkSdKQa5tEKmdbuxi4MzPf2bBpE7Cq3F4FXNFQ/uqoHAc8VKa9XQ2cEBEHlQW1TyhlkiRJkiRJGnKzmc72IuBVwK0RcUspezOwDrg8Is4CvgmcVrZdBZwMbAEeAV4DkJk7I+J84IZS762ZubMrrZAkSZIkSVJPtU0iZeaXaL6eEcDxTeoncHaLfW0ANnQSoCRJGl7N1siRJEnSaJrVmkiSJEmSJEma32Z9djZJkqR+aTXCyTOBSZIkDY4jkSRJkiRJktSWI5EkSZK6wNFTkiRp1DkSSZIkSZIkSW05EkmSJInhONNcYwxrlu3mzHLf0UySJGkYmETqkmZfPP3CJ0mSnOYmSZJGhUkkSZJUG9MTMlOjdUzISJIk9Z5JJEmSpAHoZPrcMEy1kyRJMokkSZIkSdKIaHXg4SMrntznSDSKTCJJkqTa62RtQkf1SPUUERuAlwIPZOZzS9nBwGXAEmAr8PLM3BURAbwbOBl4BDgzM28uj1kF/EXZ7dsyc2M/2yFJdbbPoAOQJEmSpFn4CLBiWtla4JrMXApcU+4DnAQsLZfVwEXwaNLpPOBY4BjgvIg4qOeRS9KIMIkkSZIkaehl5heBndOKVwJTI4k2Aqc2lF+SlWuBhRFxOHAisDkzd2bmLmAzeyamJEktOJ1NkiRJUl2NZeYOgMzcERGHlfJFwH0N9baVslble4iI1VSjmBgbG2NiYqK7kffRmmW7Z1Vv7IDWdevc/lYmJyeHrl23fuuhpuXLFj21aflsX1sYzvb2ku3tDZNIkqSBi4gFwI3AtzLzpRFxJHApcDBwM/CqzPxJROwPXAL8X8D3gFdk5tYBhS1JGl7RpCxnKN+zMHM9sB5g+fLlOT4+3rXg+u3MWa4Ft2bZbt5xa/OfiFvPGO9iRMNhYmKCYXtdW71Wrf7+s31toVpYe9ja20vD+Pr2Ur/a63Q2SdIweD1wZ8P9twMXljUudgFnlfKzgF2Z+SzgwlJPkjR/3V+mqVGuHyjl24AjGuotBrbPUC5JmgVHIkmSBioiFgOnABcAf1bOqPNi4HdLlY3AW6gWRV1ZbgN8EnhvRERmNj2KrO7xjGaShtQmYBWwrlxf0VB+TkRcSrWI9kNlutvVwF81LKZ9AnBun2OWpNoyiSRJGrR3AW8EDiz3DwEezMypSf6N61U8upZFZu6OiIdK/e/2L1xJ0iBExCeAceDQiNhGdZa1dcDlEXEW8E3gtFL9KuBkYAvwCPAagMzcGRHnAzeUem/NzOmLdUuSWjCJJEkamIh4KfBAZt4UEeNTxU2q5iy2Ne63o8VQR3nhxW61rZOFO/tppkVg/+5jVzQtX7OslxF1z0xtGyZzeX/Nx/+5Zq/lqP4NeiUzX9li0/FN6iZwdov9bAA2dDE0SZo3TCJJkgbpRcBvR8TJwBOBn6MambQwIvYto5Ea16uYWstiW0TsCzyVPU/33PFiqKO88GK32tbJwp39NNMisHVXm7bd+nDT4q3rTmn5kPn4P9fsf2gUFyqWJI02F9aWJA1MZp6bmYszcwlwOvD5zDwD+ALwslJt+hoXq8rtl5X6rockSZIk9YFJJEnSMHoT1SLbW6jWPLq4lF8MHFLK/wxYO6D4JEmSpHmnBmOkJUnzQWZOABPl9j3AMU3q/IjHFk2VJEmS1EcmkSRJktR1S1qsozXTWkm92IckSeoep7NJkiRJkiSpLUciSZIkqVZajVBqxlFLkiR1j0kkSZIkSeojp2pKqiuns0mSJEmSJKktk0iSJEmSJElqyySSJEmSJEmS2jKJJEmSJEmSpLZMIkmSJEmSJKktk0iSJEmSJElqa99BByBJkqT5Y8naK1mzbDdnNpzi3NOaS5JUDyaRJEnS4yxp+HEvSZIkTWmbRIqIDcBLgQcy87ml7GDgMmAJsBV4eWbuiogA3g2cDDwCnJmZN5fHrAL+ouz2bZm5sbtNkSRJUh31MnHZat+OfpIkqXOzGYn0EeC9wCUNZWuBazJzXUSsLfffBJwELC2XY4GLgGNL0uk8YDmQwE0RsSkzd3WrIcPILy2SJEmSJGlUtF1YOzO/COycVrwSmBpJtBE4taH8kqxcCyyMiMOBE4HNmbmzJI42Ayu60QBJkiRJkiT13lzXRBrLzB0AmbkjIg4r5YuA+xrqbStlrcr3EBGrgdUAY2NjTExMdBzc5OQkf/exK/YoX7Os4131xExtmpycnFObB62ucUN9Yzfu/qtz7JIkSZK0t7q9sHY0KcsZyvcszFwPrAdYvnx5jo+PdxzExMQE7/jSwx0/rl+2njHectvExARzafOg1TVuqG/sxt1/dY5dkiRJ9eVJLzQs5ppEuj8iDi+jkA4HHijl24AjGuotBraX8vFp5RNzfG5JktTC9C+Za5btflwHLEnSXLjeqySYexJpE7AKWFeur2goPyciLqVaWPuhkmi6GviriDio1DsBOHfuYUuSpNnyi78kSZK6oW0SKSI+QTWK6NCI2EZ1lrV1wOURcRbwTeC0Uv0q4GRgC/AI8BqAzNwZEecDN5R6b83M6Yt1S5IkSZKkIeWBKbVNImXmK1tsOr5J3QTObrGfDcCGjqKTJKnG/KIlDa9m/5/+b0qSNLNuL6wtSZJqwkU6JUmS1Il9Bh2AJEmSJO2NiNgaEbdGxC0RcWMpOzgiNkfE3eX6oFIeEfGeiNgSEV+NiBcMNnpJqg+TSJIkSZJGwW9k5tGZubzcXwtck5lLgWvKfYCTgKXlshq4qO+RSlJNmUSSJA1MRBwREV+IiDsj4vaIeH0p9+ixJGlvrQQ2ltsbgVMbyi/JyrXAwog4fBABSlLduCaSJGmQdgNrMvPmiDgQuCkiNgNnUh09XhcRa6mOHr+Jxx89Ppbq6PGxA4lckjRMEvhcRCTw95m5HhjLzB0AmbkjIg4rdRcB9zU8dlsp29G4w4hYTTVSibGxMSYmJroW7Jplu5uWd/M5ZvN8040dMPu6U3oVcz9MTk4OXfyd/v070Ul7b/3WQ03L1yxrXn/Y/o4wnK9vL/WrvSaRJEkDU77cT33B/0FE3En1RX4lMF6qbQQmqJJIjx49Bq6NiIURcfjUj4RR5OLXkjQrL8rM7SVRtDkivjZD3WhSlnsUVImo9QDLly/P8fHxrgQKcGars3ee0b3nmM3zTbdm2W7ecWtnPxF7FXM/TExM0M3XtRtm+1rNxUdWPHnW7e00jmF8Hwzj69tL/WqvSSRJ0lCIiCXA84Hr6PPR414duenGkea9PSI5l6PKdTLK7bNt/deNz4FWnyfN2jufjpD3WmZuL9cPRMRngGOA+6cONJTpag+U6tuAIxoevhjY3teAJammTCJJkgYuIp4CfAp4Q2Z+P6LZQeKqapOyvT563KsjN9040ry3RyTnclS5Tka5fbZtAG59uGnx1nWnzHoXrT5Pmv0vD+OR+zqKiCcD+5QRrU8GTgDeCmwCVgHryvUV5SGbgHMi4lKqKdEPjfKIVvVXsxHEnXyG9NKt33qo+WfRkMSnehjC3luSNJ9ExBOoEkgfy8xPl+LaHT122pkkDcwY8JlyAGJf4OOZ+c8RcQNweUScBXwTOK3Uvwo4GdgCPAK8pv8h9479kaReMokkSRqYqL7xXwzcmZnvbNjk0WNJ0qxk5j3A85qUfw84vkl5Amf3ITRJGjkmkSRJg/Qi4FXArRFxSyl7M1XyyKPHkobCME9PkSSpn0wiSZIGJjO/RPN1jsCjx5KkEeABAkmjxCTSALTqSDyiJUmSJEmShpVJJEmS+syj0pIkSaqjfQYdgCRJkiRJkoafI5EkSZIkSZqnHCGtTjgSSZIkSZIkSW05EkmSJEnqUKsj92uW7eZMj+pLkkaUI5EkSZIkSZLUliORhsiStVfucfRq67pTBhiRJEmSJElSxZFIkiRJkiRJasuRSJIkSZI0pFqtvzUsMxaGPb5h4N9Io8QkkiRJkiRJHWiVGJJGnUkkSZIkSRoCJiYkDTvXRJIkSZIkSVJbjkSSJEmSJEl90WzEnetD1YdJpCHnImySJEmSVPH3kTRYJpEkSZIkSUNlPqwPNeptHPX2zVcmkSRJkiRJasJEiPR4JpFqymGcktR7fnGUJElqz+9M84dJJEmSJElSV3WSVBiWA+EmQqT2TCJJkiRJUs2Y8NB81qszvDnjpz2TSJIkSZIkaWBM3tSHSaQRU8dho5IkSZIkDSuTXI8xiSRJkiRJGph+T82ber41y3ZzptMCpY6YRJrHejWPVJIkSZL6yd82o8m1v4ZP35NIEbECeDewAPhQZq7rdwxqzWF6kurAvkSStLfsS6T5oZeJqPmYvOxrEikiFgDvA14CbANuiIhNmXlHP+NQ50wuSRoW9iWSpL1lXzI/OIplfpn+ejtdsTf6PRLpGGBLZt4DEBGXAisBP6xrqo7ziU18SbVnXyJJ2lv2JZJ6ot/Jy37/vo3M7N+TRbwMWJGZryv3XwUcm5nnNNRZDawud38JuGsOT3Uo8N29DHdQ6hp7XeOG+sZu3P3Xjdh/PjOf1o1g5qse9SV1fl+2M8ptg9Fun22rp360zb5kL/Xxd0ndjPL/ZjO2d7TZ3pnNqS/p90ikaFL2uCxWZq4H1u/Vk0TcmJnL92Yfg1LX2OsaN9Q3duPuvzrHPmK63peM8ms7ym2D0W6fbaunUW7biOnL75K6mW/vX9s72mxvb+zT6yeYZhtwRMP9xcD2PscgSao3+xJJ0t6yL5GkOeh3EukGYGlEHBkR+wGnA5v6HIMkqd7sSyRJe8u+RJLmoK/T2TJzd0ScA1xNdSrNDZl5ew+eqs7DTusae13jhvrGbtz9V+fYR0aP+pJRfm1HuW0w2u2zbfU0ym0bGX38XVI38+39a3tHm+3tgb4urC1JkiRJkqR66vd0NkmSJEmSJNWQSSRJkiRJkiS1NXJJpIhYERF3RcSWiFg7BPFsiIgHIuK2hrKDI2JzRNxdrg8q5RER7ymxfzUiXtDwmFWl/t0RsaoPcR8REV+IiDsj4vaIeH2NYn9iRFwfEV8psf9lKT8yIq4rcVxWFlEkIvYv97eU7Usa9nVuKb8rIk7sdezlORdExJcj4rN1iTsitkbErRFxS0TcWMqG/r1SnnNhRHwyIr5W3u+/VpfY1R3D1m/sjU76nLrptF+qk077rTqabd9WR530gdIwafXZ07D97yJiclDxddsMn7URERdExNdLH/Mng461G2Zo7/ERcXP5zPpSRDxr0LF2yyj3Nc00ae/Hynfa28p3wif05Ikzc2QuVIvifQN4JrAf8BXgqAHH9OvAC4DbGsr+Blhbbq8F3l5unwz8ExDAccB1pfxg4J5yfVC5fVCP4z4ceEG5fSDwdeComsQewFPK7ScA15WYLgdOL+UfAP6w3P4j4APl9unAZeX2UeU9tD9wZHlvLejDe+bPgI8Dny33hz5uYCtw6LSyoX+vlOfdCLyu3N4PWFiX2L105fUfun5jL9sz6z6nbhc67JfqdOm036rjZbZ9Wx0vnfSBXrwM06XVZ0+5vxz4KDA56Dh73V7gNcAlwD5l22GDjrXH7f068OxS/kfARwYdaxfbPLJ9zSzbe3J53QP4RK/aO2ojkY4BtmTmPZn5E+BSYOUgA8rMLwI7pxWvpPrhSrk+taH8kqxcCyyMiMOBE4HNmbkzM3cBm4EVPY57R2beXG7/ALgTWFST2DMzp46aPKFcEngx8MkWsU+16ZPA8RERpfzSzPxxZt4LbKF6j/VMRCwGTgE+VO5HHeJuYejfKxHxc1Q/ui8GyMyfZOaDdYhdXTN0/cbe6LDPqZU59Eu1MYd+q1Y67NtGRe3flxp9rT57ImIB8D+BNw4suB6Y4bP2D4G3ZubPSr0HBhRiV83Q3gR+rpQ/Fdg+gPC6br71NdPbC5CZV5XXPYHrgcW9eO5RSyItAu5ruL+tlA2bsczcAdWXYuCwUt4q/oG2K6ppUs+nyl7XIvYytO8W4AGqH/TfAB7MzN1N4ng0xrL9IeCQAcX+LqoO+2fl/iHUI+4EPhcRN0XE6lJWh/fKM4HvAB8uQ0E/FBFPrkns6o758Nq1ej/X1iz7pVrpsN+qm076tjrqpA+Uhsr0z57MvA44B9g09R4eJS3a+wvAKyLixoj4p4hYOtgou6dFe18HXBUR24BXAesGGWMXjXpfM9309j6qTGN7FfDPvXjiUUsiRZOy7HsUc9cq/oG1KyKeAnwKeENmfn+mqk3KBhZ7Zv40M4+myr4eAzx7hjiGIvaIeCnwQGbe1Fg8QwxDEXfxosx8AXAScHZE/PoMdYcp7n2ppv5clJnPBx6mmnbQyjDFru7wtauZDvqlWumw36qNOfRtddRJHygNlemfPeX9exrwd4ONrDeatPe5VEtA/CgzlwMfBDYMMsZuatHePwVOzszFwIeBdw4yxm6YJ33No1q0t9H7gS9m5v/pxfOPWhJpG3BEw/3FDOfwvPvLFBjK9dSQyVbxD6RdJYP5KeBjmfnpUlyL2KeUqUkTVPN/F0bEvk3ieDTGsv2pVNNB+h37i4DfjoitVFNqXkyVYR72uMnM7eX6AeAzVD+A6vBe2QZsK0dloBru+gLqEbu6Yz68dq3ez7XTYb9US7Pst+qk076tdjrsA6Wh1PDZ8xvAs4At5f/2SRGxZYCh9URDe1dQfRf4VNn0GeBXBhRWzzS09yTgeQ3ffS8DXjiouLpo5PuaafZob0T8PwARcR7wNKr1knpi1JJINwBLyyrs+1EtNrxpwDE1swmYOnvTKuCKhvJXlzMEHAc8VIaRXg2cEBEHRXV2jxNKWc+UOaQXA3dmZmN2ug6xPy0iFpbbBwC/SbV2xheAl7WIfapNLwM+X+aRbgJOj+osaEcCS6nmlvZEZp6bmYszcwnVe/fzmXnGsMcdEU+OiAOnblO9xrdRg/dKZn4buC8ifqkUHQ/cUYfY1TV16Tf2Rqv3c63MoV+qjTn0W7Uxh76tVubQB0pDo8Vnz02Z+fTMXFL+bx/JzJE4e1eL9n4N+F9USQeA/0y18HTtzdC3PDUifrFUe0kpq7VR72uma9He34uI11Gt1frKqTW+ehXASF2oViT/OtVaAv9tCOL5BLAD+HeqLPdZVPMzrwHuLtcHl7oBvK/EfiuwvGE/r6VaIHkL8Jo+xP0fqYb7fRW4pVxOrknsvwJ8ucR+G/A/SvkzqZIpW4B/APYv5U8s97eU7c9s2Nd/K226Czipj++bcR5bZX+o4y7xfaVcbp/6v6vDe6U859HAjeX98r+ozq5Wi9i9dO09MFT9xl62ZdZ9Tt0unfZLdbp02m/V9TKbvq1ul077QC9ehunS6rNnWp1ROjtbq8/ahcCV5bvdv1KN1Bl4vD1s7++Utn6FanTSMwcZZw/aPXJ9TQft3V2+z059T9rjf7oblyhPJkmSJEmSJLU0atPZJEmSJEmS1AMmkSRJkiRJktSWSSRJkiRJkiS1ZRJJkiRJkiRJbZlEkiRJkiRJUlsmkSRJkiRJktSWSSRJkiRJkiS1ZRJJkiRJkiRJbZlEkiRJkiRJUlsmkSRJkiRJktSWSSRJkiRJkv5/9u49TrKyPPT974ERBBSHi7QwgxmM6Ak6J2omQHRvT0cUuajjPlGDEplRsicXiBrHyGDMRgVzxmwRMHjwTGQCRMIliGEiRB0vHbc7ARFEBySGCY7QMGFALjIQLy3P+WO9LTU9VV3d1XXv3/fzqU9Xvetdq563qnqtqme977skNWUSSZIkSZIkSU2ZRJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRFLfiogtEfHKDj/H9oh4Thu3lxHx3HZtT5IkSZKkfmESSfNaZj4tM+8EiIiLIuKsXsckSaovIj4QEZ8u959dTgTs2sHnG/rjQkSMRcTv9joOSRoUPTgWfTIi/qxT25dma0GvA5AkSZqtzLwLeFqv45AkzV/dOBZl5u93cvvSbNkTSX0vInaPiHMj4t5yOzcidi/LRiNiPCJWR8S2iNgaEW+rWXe/iPiHiPhRRNwYEWdFxNdrlmdEPDciVgEnAu8tZxP+oXZ5Tf0dzkpHxJ+U57w3It5eJ+6PRsRdEXFfOYuwR+deKUmSJEmSOsckkgbBnwJHAi8CfhU4HHh/zfJnAc8AFgEnA5+IiH3Ksk8Aj5U6K8ptJ5m5DrgU+IsyxO21zYKKiGOA9wCvAg4Fps7f9BHgeSXu55b4/kez7UrSoCtz2v1JRHwnIh6LiAsjYiQi/jEiHo2IL03upyPiyIj454h4OCK+HRGjNds5JCL+qayzEdi/ZtmSkuhfUB6/LSJuL3XvjIjfq6k77QmHJvaJiGvLdm+IiF+u2e5LywmKR8rfl055DV5Z87h2+MNTI+LTEfHD0u4bI2KkLHtGeb22RsQ95eRHw2ES5YTFwxHxwpqyZ0bEf0bEARGxT0R8LiLuj4iHyv3FDbb1ixgbvMazik2SemlYjkVRcxK72TYiYo+IODsiflCOTV+PchI7Il4XEbeVNo5FxK+08lo1e700/EwiaRCcCHwoM7dl5v3AB4G31iz/WVn+s8y8DtgOPL98sf0t4IzMfDwzvwtc3Ma43gT8dWbempmPAR+YXBARAfx34I8z88HMfBT4c+CENj6/JPWz36JKsj8PeC3wj8D7qL587wK8IyIWAdcCZwH7UiXmPxMRzyzb+FvgprLOmTQ4EVBsA14D7A28DTgnIl5Ss3y6Ew7TeTPVcWcfYDPwYYCI2LfE/nFgP+BjwLURsd8MtrmixHJwWff3gf8syy4GJqhOPrwYOBpoOGdRZv4EuLrEOelNwD9l5jaq1/qvgV8Cnl2e5/wZxFjPrGKTpD4wLMeiWtNt46PArwEvLW15L/BERDwPuAx4F/BM4DrgHyJit5rtNn2tAGbwemnImUTSIDgI+EHN4x+Uskk/zMyJmsePU41NfibVvF931yyrvd+OuGq3VxvjM4E9gZtKhv5h4POlXJLmg7/MzPsy8x7gfwE3ZOa3StLjs1RJiN8BrsvM6zLziczcCHwTOC4ing38OvBnmfmTzPwa8A+Nniwzr83Mf8/KPwFfBP5rTZW6Jxxm0I6rM/Mb5ThzKVXvUoDjgTsy828ycyIzLwP+leqLdzM/o0oePTczf56ZN2Xmj0pvpGOBd2XmYyUJdA7NT0D8LTsmkd5SysjMH2bmZ8rJlEepkmD/1wxi3MEcYpOkXhqWY1GtRifQdwHeDrwzM+8px5d/Lm39beDazNyYmT+jSjbtQZVsms1rxXSv1yzboQHlxNoaBPdSnUG9rTx+dilr5n6qM6aLgX8rZQdPUz/rlD1OlQya9CxgvNzfOmV7z665/wDV2d4XlB2xJM0399Xc/886j59GtW9/Y0TUJl6eAnyVKlH/UOnpOekHNNiPR8SxwBlUZ1B3odp3b6qp0uiEQzP/0WCdqSc4JuNbNINt/g1VOy6PiIXAp6mGbv8SVfu3Vh1agaotzU6AfAXYIyKOKPG+iOoLPxGxJ1Wy5xiq3lQAT4+IXTPz5zOIdVKrsUlSLw3LsahWo23sDzwV+Pc66+xwzMrMJyLibnY8Zs3ktYLpXy/NA/ZE0iC4DHh/meNhf6p5hT7dZB3Kl+OrgQ9ExJ4R8X8AJ02zyn3Ac6aU3QK8JSJ2jWoOpNqzt1cCKyPisPIl/Yya534C+CuqLqwHQNX1MyJe3SxuSZpH7gb+JjMX1tz2ysy1VIn6fSJir5r6z66322QndAAAIABJREFUkagutvAZqjOrI5m5kKqrftSr3yaTJzhqPRuYPHHwGDufhACgnD3+YGYeRnUW+DVUx6e7gZ8A+9e8Hntn5gumC6Qcc66k6o30FuBzpdcRwGqqs9xHZObewMtLeb3XpmHMrcYmSQNgkI9FtR4Afgz8cp1lOxyzytQbB/PkMWs2pnu9NA+YRNIgOIuqi+R3qDL5N5eymTiVaszwf1Cd+b2M6ktwPRcCh5XhZ39fyt5JNTThYaq5mSbLycx/BM6lOgO8ufytdVopvz4ifgR8idl3V5WkYfZp4LUR8eqSrH9qmTR0cWb+gGrf/8GI2C0i/guNh4rtBuxO6YFazgQf3eHYrwOeFxFviYgFEfHbwGHA58ryW4ATIuIpEbEMeMPkihHxmxGxtMzd9yOqoQk/z8ytVEMfzo6IvSNil4j45YiYyfCzv6UarnBiuT/p6VRnkB8u8zidUWfdSbcAL4+IZ0fEM4DTJxfMMTZJ6meDfCz6hXJCYT3wsYg4qLTlN0py60rg+Ig4KiKeQnWC4SfAP7fwVA1fr7Y1Rn3NJJL6VmYuycwvZeaPM/MdmXlgub0jM39c6oxl5uJ665X792fm8eVs6a+XKuM1dSMzN5f7d2Tmi0o2/fWl7JuZ+YLMfHpmvjUz35yZ769Zf21mPiszD8rM9VO29+PMfF9mPqc8/69k5sc7+qJJ0gDJzLuB5VQTd95PdXbzT3jy+8lbgCOAB6mSH5c02M6jVBN+Xgk8VNbb0OHYf0jVg2g18EOqyUtfk5kPlCp/RnU2+CGqiblrEzvPAq6iSiDdDvwTT/awPYnqh8h3y7pXAQfOIJ4bqHoSHUQ1Geqkc6nmvXgAuJ5qfr5G29gIXEF10uYmnkyITWopNknqZ4N8LKrjPVQn3W+kivcjwC6Z+T2quYz+kup48FrgtZn509k+wQxeLw25yKw3DYw0HMoQtt2odqa/TnXm+Hcz8++nXVGSJEmSJO3AibU17J5ONYTtIKpLbp4NXNPTiCRJkiRJGkD2RJIkSfNWRNzGzhNkA/xeZl7a7XgaiYhPUg1FmOrTmfn73Y5HktQ+g3IsksAkkiRJkiRJkmagr4ez7b///rlkyZKObf+xxx5jr732al5xgA17G23fYJuP7bvpppseyMxn9iikeanRsWTYP3+TbOdwsZ3DpdV2eizpvnrHkn7+nBrb7PVrXGBsrTK26bV6LOnrJNKSJUv45je/2bHtj42NMTo62rHt94Nhb6PtG2zzsX0R8YPeRDN/NTqWDPvnb5LtHC62c7i02k6PJd1X71jSz59TY5u9fo0LjK1Vxja9Vo8lXoZPkiRJkiRJTZlEkiRJkiRJUlMmkSRJkiRJktSUSSRJkiRJkiQ1ZRJJkiRJkiRJTZlEkiRJkiRJUlNNk0gRcXBEfDUibo+I2yLinaX8AxFxT0TcUm7H1axzekRsjojvRcSra8qPKWWbI2JNZ5okSZIkSZKkdlswgzoTwOrMvDking7cFBEby7JzMvOjtZUj4jDgBOAFwEHAlyLieWXxJ4BXAePAjRGxITO/246GSJIkSZIkqXOa9kTKzK2ZeXO5/yhwO7BomlWWA5dn5k8y8/vAZuDwctucmXdm5k+By0tdSZIkSZpWRKyPiG0RceuU8j8qox1ui4i/qCl3dIQktdlMeiL9QkQsAV4M3AC8DDg1Ik4CvknVW+khqgTT9TWrjfNk0unuKeVH1HmOVcAqgJGREcbGxmYT4qxs3769o9vvB8PexkFu36Z7HtmpbOmiZ+zweJDbNxO2T5I0XyxZc+1OZRcds1cPIhloFwHnA5dMFkTEb1KdmP4/M/MnEXFAKXd0RJvV+wwDbFl7fJcjkdRLM04iRcTTgM8A78rMH0XEBcCZQJa/ZwNvB6LO6kn9Xk+5U0HmOmAdwLJly3J0dHSmIc7a2NgYndx+Pxj2Ng5y+1bWORBvOXF0h8eD3L6ZsH2SJGmmMvNr5aR2rT8A1mbmT0qdbaX8F6MjgO9HxOToCCijIwAiYnJ0hEkkSZqBGSWRIuIpVAmkSzPzaoDMvK9m+V8BnysPx4GDa1ZfDNxb7jcqlyRJkqTZeh7wXyPiw8CPgfdk5o3McXQENB8h0c89jjsR2+qlE3XLZ/s8/fq69WtcYGytMrbOaJpEiogALgRuz8yP1ZQfmJlby8P/BkyOTd4A/G1EfIyq6+ihwDeoeigdGhGHAPdQdS99S7saIknqXxGxHngNsC0zX1jK9gWuAJYAW4A3ZeZD5bhzHnAc8DiwcnJuvohYAby/bPaszLy4m+2QJPWdBcA+wJHArwNXRsRzmOPoCGg+QqKfexzPNLbZDFGr14sedu5J30y/vm79GhcYW6uMrTOaTqxNNffRW4FXRMQt5XYc8BcRsSkivgP8JvDHAJl5G3AlVZfQzwOnZObPM3MCOBX4AtXk3FeWupKk4XcRcMyUsjXAlzPzUODL5THAsVQnIA6lOgN8Afwi6XQG1Rnjw4EzImKfjkcuSepn48DVWfkG8ASwP41HR0w3akKS1ETTnkiZ+XXqZ/Kvm2adDwMfrlN+3XTrSZKGU4N5LJYDo+X+xcAYcFopvyQzE7g+IhZGxIGl7sbMfBAgIjZSJaYu63D4kqT+9ffAK4CxMnH2bsADODpCkjpiVldnkySpjUYmh0Vn5tbJK+pQzVkxdb6KRdOU72QmV/oc5LHos2E7h4vtHFz15pMZxnZ2UkRcRnVCYf+IGKfqnboeWB8RtwI/BVaUkxC3RcTk6IgJyuiIsp3J0RG7AusdHSFJM2cSSZLUbxrNY9GofOfCGVzpc5DHos+G7RwutnNw1ZtP5qJj9hq6dnZSZr65waLfaVDf0RGS1GYzmRNJkqROuK8MU6P8nbwss/NYSJIkSX3IJJIkqVc2ACvK/RXANTXlJ0XlSOCRMuztC8DREbFPmVD76FImSZIkqQscziZJ6rgG81ispboU88nAXcAbS/XrgOOAzcDjwNsAMvPBiDgTuLHU+9DkJNuSJEmSOs8kkiSp46aZx+KoOnUTOKXBdtZTTaIqSZIkqctMIkmSJEmSfmFJnYngJQlMIkmSJEmSWtQo4bRl7fFdjkRSN5hEkiRJXeEPDUmSpMHm1dkkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU0t6HUAkiRpuCxZc22vQ5AkSVIH2BNJkiRJkiRJTZlEkiRJkiRJUlMmkSRJkiT1vYhYHxHbIuLWOsveExEZEfuXxxERH4+IzRHxnYh4SU3dFRFxR7mt6GYbJGnQmUSSJEmSNAguAo6ZWhgRBwOvAu6qKT4WOLTcVgEXlLr7AmcARwCHA2dExD4djVqShohJJEmSJEl9LzO/BjxYZ9E5wHuBrClbDlySleuBhRFxIPBqYGNmPpiZDwEbqZOYkiTV59XZJEmSJA2kiHgdcE9mfjsiahctAu6ueTxeyhqV19v2KqpeTIyMjDA2NrbD8u3bt+9U1i9mGtvqpRMdi6He82+65xFG9oC/vPSaHcqXLnpGx+KYqWF4P3vB2FrTz7E1YxJJkiRJ0sCJiD2BPwWOrre4TllOU75zYeY6YB3AsmXLcnR0dIflY2NjTC3rFzONbeWaazsWw5YTd37+lWuuZfXSCc7etKBp3W4bhvezF4ytNf0cWzMOZ5MkSZI0iH4ZOAT4dkRsARYDN0fEs6h6GB1cU3cxcO805ZKkGbAnkiRJQ2TJlLPKq5dOMNqbUCSpozJzE3DA5OOSSFqWmQ9ExAbg1Ii4nGoS7Ucyc2tEfAH485rJtI8GTu9y6JI0sOyJJEmSJKnvRcRlwL8Az4+I8Yg4eZrq1wF3ApuBvwL+ECAzHwTOBG4stw+VMknSDNgTSZIkSVLfy8w3N1m+pOZ+Aqc0qLceWN/W4Prc1F6qktQqeyJJkiRJkiSpKZNIkiRJkiRJasrhbJIkSZI0JJasuZbVSydY6RA2SR1gTyRJkiRJkiQ1ZRJJkiRJkiRJTZlEkiRJkiRJUlNN50SKiIOBS4BnAU8A6zLzvIjYF7gCWAJsAd6UmQ9FRADnAccBjwMrM/Pmsq0VwPvLps/KzIvb2xxJktQJjS4PvWXt8V2ORJIkSb0yk55IE8DqzPwV4EjglIg4DFgDfDkzDwW+XB4DHAscWm6rgAsAStLpDOAI4HDgjIjYp41tkSRJkiRJUoc0TSJl5tbJnkSZ+ShwO7AIWA5M9iS6GHh9ub8cuCQr1wMLI+JA4NXAxsx8MDMfAjYCx7S1NZIkSZIkSeqIpsPZakXEEuDFwA3ASGZuhSrRFBEHlGqLgLtrVhsvZY3Kpz7HKqoeTIyMjDA2NjabEGdl+/btHd1+Pxj2Ng5y+1YvndipbGpbBrl9M2H7JEmSJGlwzDiJFBFPAz4DvCszf1RNfVS/ap2ynKZ8x4LMdcA6gGXLluXo6OhMQ5y1sbExOrn9fjDsbRzk9q2sM7/IlhNHd3g8yO2bCdsnqRHnYJIkSeo/M0oiRcRTqBJIl2bm1aX4vog4sPRCOhDYVsrHgYNrVl8M3FvKR6eUj7UeuiRpGETEHwO/S3ViYRPwNuBA4HJgX+Bm4K2Z+dOI2J3qYg+/BvwQ+O3M3NKLuIdBOxI1jbYhSZKk4dN0TqRytbULgdsz82M1izYAK8r9FcA1NeUnReVI4JEy7O0LwNERsU+ZUPvoUiZJmqciYhHwDmBZZr4Q2BU4AfgIcE65eMNDwMlllZOBhzLzucA5pZ4kSZKkLphJT6SXAW8FNkXELaXsfcBa4MqIOBm4C3hjWXYdcBywGXic6owymflgRJwJ3FjqfSgzH2xLKyRJg2wBsEdE/AzYE9gKvAJ4S1l+MfABqqt9Li/3Aa4Czo+IyMydhkcPO3sASZL6mccpaTg1TSJl5tepP58RwFF16idwSoNtrQfWzyZASdLwysx7IuKjVCcj/hP4InAT8HBmTs4+X3shhl9cpCEzJyLiEWA/4IHa7c7kIg2DPvF5vcn56xnZY+dJ+5tto179mT5fK2bzfI3aMujv50zZzsFV7zM9jO2UJA23WV2dTZKkdirDm5cDhwAPA38HHFun6mRPo7ZdpGHQJz6vNzl/PauXTvCmBu1stI2pk/zP5vlaMZvnq1cXBv/9nCnbObjqfaYvOmavoWunJGm4NZ0TSZKkDnol8P3MvD8zfwZcDbwUWBgRkyc6Ji/QADUXbyjLnwE4NFqSJEnqAnsiSZJ66S7gyIjYk2o421HAN4GvAm+gukLb1Is3rAD+pSz/ynycD2nYOG+GJEnSYLAnkiSpZzLzBqoJsm8GNlEdl9YBpwHvjojNVHMeXVhWuRDYr5S/G1jT9aAlST0REesjYltE3FpT9j8j4l8j4jsR8dmIWFiz7PSI2BwR34uIV9eUH1PKNkeExxFJmgV7IkmSeiozzwDOmFJ8J3B4nbo/5smrgUqS5peLgPOBS2rKNgKnl4stfAQ4HTgtIg4DTgBeABwEfCkinlfW+QTwKqoh0jdGxIbM/G6X2iBJA82eSJIkSZL6XmZ+jSnz4GXmF2uu5nk91Tx6UF204fLM/Elmfh/YTHVy4nBgc2bemZk/pRo2vbwrDZCkIWBPJEmSJEnD4O3AFeX+Iqqk0qTxUgZw95TyI+ptLCJWAasARkZGGBsb22H59u3bdyrrB6uXTjCyR/W3H9WLrR9ex359P8HYWmVsnWESSZIkSdJAi4g/BSaASyeL6lRL6o/EqHuBhsxcRzVPH8uWLcvR0dEdlo+NjTG1rB+sXHMtq5dOcPam/vypVy+2LSeO9iaYGv36foKxtcrYOqM/9yySJEmSNAMRsQJ4DXBUzRU7x4GDa6otBu4t9xuVS5KacE4kSZIkSQMpIo6huqLn6zLz8ZpFG4ATImL3iDgEOBT4BnAjcGhEHBIRu1FNvr2h23FL0qCyJ5IkSZKkvhcRlwGjwP4RMU51Zc/Tgd2BjREBcH1m/n5m3hYRVwLfpRrmdkpm/rxs51TgC8CuwPrMvK3rjZGkAWUSSZIkSVLfy8w31ym+cJr6HwY+XKf8OuC6NoYmSfOGw9kkSZIkSZLUlEkkSZIkSZIkNWUSSZIkSZIkSU2ZRJIkSZIkSVJTTqwtddiSNdf2OgRJkiRJkubMJJIkSdqByW9JkiTVYxJJkqQhZ1JIkiRJ7WASSeojU3/orV46wco117Jl7fE9ikiS+kujhNhFx+zV5UgkSZLmHyfWliRJkiRJUlMmkSRJkiRJktSUSSRJkiRJkiQ1ZRJJkiRJkiRJTZlEkiRJkiRJUlNenU2SpD5R78pjXp1RkiRJ/cKeSJIkSZIkSWrKJJIkSZIkSZKacjibJEmaV+oNGwSHDkqSJDVjTyRJkiRJkiQ1ZRJJkiRJUt+LiPURsS0ibq0p2zciNkbEHeXvPqU8IuLjEbE5Ir4TES+pWWdFqX9HRKzoRVskaVA5nE2SJEnSILgIOB+4pKZsDfDlzFwbEWvK49OAY4FDy+0I4ALgiIjYFzgDWAYkcFNEbMjMh7rWijZpNDRXkjrJnkiSJEmS+l5mfg14cErxcuDicv9i4PU15Zdk5XpgYUQcCLwa2JiZD5bE0UbgmM5HL0nDwZ5IkiRJkgbVSGZuBcjMrRFxQClfBNxdU2+8lDUq30lErAJWAYyMjDA2NrbD8u3bt+9U1k2rl040XDayx/TLe6lebL18HSf1+v2cjrG1xtg6o2kSKSLWA68BtmXmC0vZB4D/Dtxfqr0vM68ry04HTgZ+DrwjM79Qyo8BzgN2BT6VmWvb2xRJkiRJAiDqlOU05TsXZq4D1gEsW7YsR0dHd1g+NjbG1LJuWjnNcLbVSyc4e1N/9heoG9umx3aq1+0rZvb6/ZyOsbXG2DpjJsPZLqJ+F89zMvNF5TaZQDoMOAF4QVnn/42IXSNiV+ATVGOTDwPeXOpKkiRJUqvuK8PUKH+3lfJx4OCaeouBe6cplyTNQNMkUoOxx40sBy7PzJ9k5veBzcDh5bY5M+/MzJ8Cl5e6kiRJktSqDcDkFdZWANfUlJ9UrtJ2JPBIGfb2BeDoiNinXMnt6FImSZqBufRxPDUiTgK+CawuE9MtAq6vqVM7xnjq2OMj6m202djjdhrkcYgzNextHIT2zWU8+uSY8X5vY6sG4f2bi2FvX7tExELgU8ALqYYUvB34HnAFsATYArwpMx+KiKAaGn0c8DiwMjNv7kHYGgBeuUgaLhFxGTAK7B8R41RXWVsLXBkRJwN3AW8s1a+jOlZspjpevA0gMx+MiDOBG0u9D2XmTE+YS9K812oS6QLgTKov+2cCZ1N96W80xrhej6eWxh630yCPQ5ypYW/jILRvuvHqzUyOGd9y4mj7Auojg/D+zcWwt6+NzgM+n5lviIjdgD2B9zGLSzb3JmxJUjdl5psbLDqqTt0ETmmwnfXA+jaGJknzxkzmRNpJZt6XmT/PzCeAv6IargaOPZYkzUJE7A28HLgQIDN/mpkPM/tLNkuSJEnqsJaSSFO+sP834NZyfwNwQkTsHhGHUJ0p/gZVd9FDI+KQcpb5hFJXkjS/PYfqSp9/HRHfiohPRcReTLlkM9Dsks2SJEmSOqzpcLYGY49HI+JFVEPStgC/B5CZt0XElcB3gQnglMz8ednOqVST1u0KrM/M29reGknSoFkAvAT4o8y8ISLOoxq61siMLs08k/n1+nHOqnpzqDWKcabzrU3OrTbs6r2fs213v30e6unHz20nDGM7630eh7GdkqTh1jSJ1GDs8YXT1P8w8OE65ddRTXAnSdKkcWA8M28oj6+iSiLdFxEHZubWGV6yeQczmV+vH+esqjeHWqM50WY639rk3GrD7qJj9trp/ZztnHSDMP9cP35uO2EY21nv81jvcyup0ujiCFvWHt/lSCTVamk4myRJ7ZCZ/wHcHRHPL0VHUfVmne0lmyVJkiR12PCfmpQk9bs/Ai4tc+bdSXUZ5l2YxSWbJUmSJHWeSSRJUk9l5i3AsjqLZnXJZmmu6g2dcNiEJEnSkxzOJkmSJEmSpKZMIkmSJEmSJKkpk0iSJEmSJElqyiSSJEmSJEmSmjKJJEmSJEmSpKZMIkmSJEmSJKmpBb0OQJIkNVbvsvOSJElSL5hEGjJTf2ysXjrBaG9CkSRJkiRJQ8QkkiRJXWbvIkmSJA0ik0gDyh8gkiRJkiSpm5xYW5IkSdJAi4g/jojbIuLWiLgsIp4aEYdExA0RcUdEXBERu5W6u5fHm8vyJb2NXpIGh0kkSZIkSQMrIhYB7wCWZeYLgV2BE4CPAOdk5qHAQ8DJZZWTgYcy87nAOaWeJGkGHM4mSZIkadAtAPaIiJ8BewJbgVcAbynLLwY+AFwALC/3Aa4Czo+IyMzsZsBqTaNpPbasPb7LkUjzkz2RJEmSJA2szLwH+ChwF1Xy6BHgJuDhzJwo1caBReX+IuDusu5Eqb9fN2OWpEFlTyRJkqQGPOMt9b+I2Ieqd9EhwMPA3wHH1qk62dMopllWu91VwCqAkZERxsbGdli+ffv2ncq6afXSiYbLRvaYfnkvdSq2ub4XvX4/p2NsrTG2zjCJJEmSBt6mex5hpVculearVwLfz8z7ASLiauClwMKIWFB6Gy0G7i31x4GDgfGIWAA8A3hw6kYzcx2wDmDZsmU5Ojq6w/KxsTGmlnXTdPu81UsnOHtTf/7U61RsW04cndP6vX4/p2NsrTG2znA4myRJkqRBdhdwZETsGREBHAV8F/gq8IZSZwVwTbm/oTymLP+K8yFJ0syYRJIkSZI0sDLzBqoJsm8GNlH9xlkHnAa8OyI2U815dGFZ5UJgv1L+bmBN14OWpAHVn30cJUmSJGmGMvMM4IwpxXcCh9ep+2Pgjd2IS5KGjT2RJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSU06sLUmS1EFL1lxbt3zL2uO7HIkkSdLc2BNJkiRJkiRJTdkTSZIkSZI00Or1+rTHp9R+9kSSJEmSJElSU/ZEkuYJz85IkiRJkubCnkiSJEmSJElqyp5IkiRJs9ToimuSJEnDzCSSJEmSJPUpk9aS+knTJFJErAdeA2zLzBeWsn2BK4AlwBbgTZn5UEQEcB5wHPA4sDIzby7rrADeXzZ7VmZe3N6mSMOr0ZcH5zSSJEmSJHXLTHoiXQScD1xSU7YG+HJmro2INeXxacCxwKHldgRwAXBESTqdASwDErgpIjZk5kPtasiw8syDJEmSJEnqB00n1s7MrwEPTileDkz2JLoYeH1N+SVZuR5YGBEHAq8GNmbmgyVxtBE4ph0NkCQNvojYNSK+FRGfK48PiYgbIuKOiLgiInYr5buXx5vL8iW9jFuSJEmaT1qdE2kkM7cCZObWiDiglC8C7q6pN17KGpXvJCJWAasARkZGGBsbazHE5rZv397R7bfD6qUTc1p/ZA/6vo1zMezv4cge068/m7bX206vX7tBeP/mYtjb12bvBG4H9i6PPwKck5mXR8QngZOpereeDDyUmc+NiBNKvd/uRcCSJEnSfNPuibWjTllOU75zYeY6YB3AsmXLcnR0tG3BTTU2NkYnt98OK+c4nG310gne1OdtnIthfw9XL53g7E2N/023nDg6pzhms34nDML7NxfD3r52iYjFwPHAh4F3l/n1XgG8pVS5GPgAVRJpebkPcBVwfkREZtY9pkiSJElqn1aTSPdFxIGlF9KBwLZSPg4cXFNvMXBvKR+dUj7W4nNrlpyUWVKfOxd4L/D08ng/4OHMnOw+V9t79Rc9WzNzIiIeKfUf6F64kiRJ0vzUahJpA7ACWFv+XlNTfmpEXE41sfYjJdH0BeDPI2KfUu9o4PTWw5YkDYOImLz6500RMTpZXKdqzmBZ7XabDo3u5XDDuQ5Vno1mw2KHxSC2s5XP33wZJjuM7az3+RzGdvZKRCwEPgW8kOq48Hbge8zyitKSpOk1TSJFxGVUvYj2j4hxqqusrQWujIiTgbuAN5bq11HtjDdT7ZDfBpCZD0bEmcCNpd6HMnPqZN2SpPnnZcDrIuI44KlUcyKdS3VhhgWlN9Jkr1Z4ssfreEQsAJ7Bzhd/mNHQ6F4ON5zrUOXZaDYsdlgMYjtbGVI8X4bJDmM76/3fX3TMXkPXzh46D/h8Zr6hXIxhT+B9zOKK0r0JW5IGS9NvW5n55gaLjqpTN4FTGmxnPbB+VtFJkoZaZp5O6ZlaeiK9JzNPjIi/A94AXM7OPV5XAP9Sln/F+ZAkaX6LiL2BlwMrATLzp8BPI2I5T06pcTHVdBqnUXNFaeD6iFg4OVVHl0OXpIEzWKfsJEnzxWnA5RFxFvAt4MJSfiHwNxGxmaoH0gk9ik+S1D+eA9wP/HVE/CpwE9VVP2d7RekdkkjNhkZ3azhiK0N1+3mIbzdjm83708/DS42tNcbWGSaRpHnMSdfVTzJzjHLRhcy8Ezi8Tp0f8+QQakmSoPpN8xLgjzLzhog4j2roWiMzml+v2dDobg27bGUIdD8P8e1mbLMZNtzPw2iNrTXG1hm79DoASZIkSZqDcWA8M28oj6+iSirdV64kzQyvKC1JaqI/09OSJEmSNAOZ+R8RcXdEPD8zv0c1d+t3y23GV5TuQejqMHvdS+1nEkmSJEnSoPsj4NJyZbY7qa4SvQuzuKK0JKk5k0iSJEmSBlpm3gIsq7NoVleUliRNzzmRJEmSJEmS1JRJJEmSJEmSJDVlEkmSJEmSJElNmUSSJEmSJElSUyaRJEmSJEmS1JRXZ5MkSeqBJWuu3alsy9rjexCJJEnSzNgTSZIkSZIkSU3ZE0mSJKlP1OudBPZQkiRJ/cGeSJIkSZIkSWrKJJIkSZIkSZKacjibJEmSJGne84IHUnP2RJIkSZIkSVJTJpEkSZIkSZLUlEkkSZIkSZIkNeWcSJIkSdpBvXlBwLlBJEma7+yJJEnVSE1mAAAgAElEQVSSJEmSpKZMIkmSJEmSJKkpk0iSJEmSJElqyiSSJEmSpIEXEbtGxLci4nPl8SERcUNE3BERV0TEbqV89/J4c1m+pJdxS9IgcWJt7cCJNCVJkjSg3gncDuxdHn8EOCczL4+ITwInAxeUvw9l5nMj4oRS77d7EbAkDRqTSJoRk0uSJEnqVxGxGDge+DDw7ogI4BXAW0qVi4EPUCWRlpf7AFcB50dEZGZ2M2ZJGkQmkSRJ6pBGCXhJUtudC7wXeHp5vB/wcGZOlMfjwKJyfxFwN0BmTkTEI6X+A7UbjIhVwCqAkZERxsbGdnjC7du371TWCauXTjSvNMXIHq2t1w39ENtfXnrNTmVVXDvX7cZ73Ey3PmutMLbW9HNszZhEkiRJmsdMdmrQRcRrgG2ZeVNEjE4W16maM1j2ZEHmOmAdwLJly3J0dHSH5WNjY0wt64SVLfyPrl46wdmb+vOnXr/G1iiuLSeOdj+YKbr1WWuFsbWmn2Nrpv/+eyVJktR2Jos0xF4GvC4ijgOeSjUn0rnAwohYUHojLQbuLfXHgYOB8YhYADwDeLD7YUvS4PHqbJIkSZIGVmaenpmLM3MJcALwlcw8Efgq8IZSbQUwOYZpQ3lMWf4V50OSpJkxiSRJkiRpGJ1GNcn2Zqo5jy4s5RcC+5XydwNrehSfJA0ch7NJkiQNEYetaT7LzDFgrNy/Ezi8Tp0fA2/samCSNCRMIkkDrN4PhS1rj+9BJJKkXjBhJEmSumlOSaSI2AI8CvwcmMjMZRGxL3AFsATYArwpMx+KiADOA44DHgdWZubNc3l+SZIkSRoWJoYl9bt29ET6zcx8oObxGuDLmbk2ItaUx6cBxwKHltsRwAXlryRpnoqIg4FLgGcBTwDrMvM8T0hIO5r8Ybl66URLl/uWJElqh05MrL0cuLjcvxh4fU35JVm5nuqSmwd24PklSYNjAlidmb8CHAmcEhGH8eQJiUOBL/PkpKe1JyRWUZ2QkCRJktQFc+2JlMAXIyKB/y8z1wEjmbkVIDO3RsQBpe4i4O6adcdL2dbaDUbEKqofBoyMjDA2NjbHEBvbvn17R7ffDquXTsxp/ZE9Gm+jXttn+3y9fv2G/T2c7v1rpNHrMZvtdOs1HYT3by6GvX3tUI4Xk8eMRyPidqpjw3JgtFS7mGqS1NOoOSEBXB8RCyPiwMnjjiRJkqTOmWsS6WWZeW9JFG2MiH+dpm7UKcudCqpE1DqAZcuW5ejo6BxDbGxsbIxObr8d5tplffXSCc7eVP9t3nLi6Jyfr942umnY38Pp3r9GGr0ns4mjW+/rILx/czHs7Wu3iFgCvBi4gS6ckOhGkm+uJwLaoZVk9CCynd3hSYbW1XvfhrGdkqThNqckUmbeW/5ui4jPUl1C877Js8JluNq2Un0cOLhm9cXAvXN5fknScIiIpwGfAd6VmT+qpj6qX7VOWUsnJLqR5OuHuWtaSUYPItvZHZ5kaF29/cFFx+w1dO2Uhk2jyc69IrLmq5a/hUTEXsAuZfjBXsDRwIeADcAKYG35e01ZZQNwakRcTjWh9iMOP5AkRcRTqBJIl2bm1aXYExJSH6r3Y8ofUpIkzR9zmVh7BPh6RHwb+AZwbWZ+nip59KqIuAN4VXkMcB1wJ7AZ+CvgD+fw3JKkIVCutnYhcHtmfqxm0eQJCdj5hMRJUTkST0hIkiRJXdNyT6TMvBP41TrlPwSOqlOewCmtPp8kaSi9DHgrsCkibill76M6AXFlRJwM3AW8sSy7DjiO6oTE48DbuhuuJEmSNH8N/+QBkqS+lZlfp/48R+AJCUmS1KecK0nz1VyGs0mSJEmSJGmeMIkkSZIkSZKkphzOJg2ZRl1rJUmSJEmaC5NIkiRJapnzgkiSNH+YRNJQqPcF1i+vkiRJkiS1j0kkzcmwJ288uypJUms8hkqSNHycWFuSJEnSwIqIgyPiqxFxe0TcFhHvLOX7RsTGiLij/N2nlEdEfDwiNkfEdyLiJb1tgSQNDnsizWNOwCxJkqQhMAGszsybI+LpwE0RsRFYCXw5M9dGxBpgDXAacCxwaLkdAVxQ/kqSmjCJJGFCTZKkXpp6HF69dIKVa6516JtmJDO3AlvL/Ucj4nZgEbAcGC3VLgbGqJJIy4FLMjOB6yNiYUQcWLYjSZqGSSQNLedikCRJml8iYgnwYuAGYGQyMZSZWyPigFJtEXB3zWrjpcwkkiQ1YRKpj9gbRpIkSWpNRDwN+Azwrsz8UUQ0rFqnLOtsbxWwCmBkZISxsbEdlm/fvn2nsrlavXSiLdsZ2aN922q3fo2tXXG1+zMBnfmstYuxtaafY2vGJJIkSZKkgRYRT6FKIF2amVeX4vsmh6lFxIHAtlI+Dhxcs/pi4N6p28zMdcA6gGXLluXo6OgOy8fGxphaNlcr23RSefXSCc7e1J8/9fo1tnbFteXE0bkHM0UnPmvtYmyt6efYmum//15JkiQNLXteq92i6nJ0IXB7Zn6sZtEGYAWwtvy9pqb81Ii4nGpC7UecD0mSZsYkktQmfimWJEnqiZcBbwU2RcQtpex9VMmjKyPiZOAu4I1l2XXAccBm4HHgbd0NV5IGl0kkSZIkzTv1Tv548Y3BlJlfp/48RwBH1amfwCkdDUrzlvsWDTuTSOo5d7SSJKker7SqYWUPdkmDyiSSJEmSBoonoCQNA/dlGkQmkdR2njWUJEmSpIo9zzRMTCKpa9x5SpKkbvP7h6RBUm+ftXrpBKPdD0Wqa5deByBJkiRJkqT+Z08kSZIkDTx7HEmS1Hn2RJIkSZIkSVJT9kSSJEmSJKmPefEi9QuTSBooU3eeq5dOsLIH3dftMi9J0vDxR5okSdMziSRJUhuYXJYkSdKwM4nUA/7Q6C1ff0mSJEnDoN5vG3tPqpNMIknaid35JUl60mxOQHmslCQNM5NI6kv2FpIkSYPI7zCSpGFmEkmSJEmSpCHhqAJ1kkkkSZIkSZLUMudm6o5+eJ1NIkmak06e6fAsiiRJGnQOcZQ0TIY2iTSTnfXqpROsXHOtP0glSZIkSZKaGNokUrf1Q7cyqZ/Yi0iSJEnqH+34fj51G5MdM2bzfHONQb3V9SRSRBwDnAfsCnwqM9d2O4Zuseuqho2fafWLXh5L/D+QpOEwn36XSNPph+82noAeHF1NIkXErsAngFcB48CNEbEhM7/bzTimmk0von74B5Pmu071/PPgNRi6eSxxny9Jw6lff5dImhlHAvVOt3siHQ5szsw7ASLicmA50Hc7a384SJ1R+781XfdXaRoDcyyRJPUtjyXSAJjN7/LZDLXrpGFPZkVmdu/JIt4AHJOZv1sevxU4IjNPramzClhVHj4f+F4HQ9ofeKCD2+8Hw95G2zfY5mP7fikzn9mLYIZFG48lw/75m2Q7h4vtHC6tttNjyRy16VjSz59TY5u9fo0LjK1Vxja9lo4l3e6JFHXKdshiZeY6YF1Xgon4ZmYu68Zz9cqwt9H2DTbbpxa15VgyX94f2zlcbOdwmS/t7FNzPpb08/tnbLPXr3GBsbXK2Dpjly4/3zhwcM3jxcC9XY5BkjTYPJZIkubKY4kktaDbSaQbgUMj4pCI2A04AdjQ5RgkSYPNY4kkaa48lkhSC7o6nC0zJyLiVOALVJfSXJ+Zt3Uzhim6Mmyux4a9jbZvsNk+zVobjyXz5f2xncPFdg6X+dLOvtOmY0k/v3/GNnv9GhcYW6uMrQO6OrG2JEmSJEmSBlO3h7NJkiRJkiRpAJlEkiRJkiRJUlPzJokUEW+MiNsi4omIWFZT/qqIuCkiNpW/r6iz7oaIuLW7Ec/ObNsXEXtGxLUR8a9lvbW9i765Vt6/iPi1Ur45Ij4eEfUu5doXpmnffhHx1YjYHhHnT1nnzaV934mIz0fE/t2PfGZabN9uEbEuIv6tfE5/q/uRz0wr7aup0/f7l2ETEcdExPfKvmFNr+NpJCLWR8S22s9HROwbERsj4o7yd59SHmU/t7nsE15Ss86KUv+OiFhRU153H9noOTrYzoPL/8nt5f/oncPY1oh4akR8IyK+Xdr5wVJ+SETcUGK4IqoJfomI3cvjzWX5kpptnV7KvxcRr64pr/vZbvQcnRQRu0bEtyLic8PazojYUj5Xt0TEN0vZUH1uVV+jz2CvNdqf9pOp+4Z+ERELI+KqqL5z3h4Rv9HrmCZFxB+X9/PWiLgsIp7aw1hm/N2kT2L7n+U9/U5EfDYiFvZLbDXL3hMRGX38W24nmTkvbsCvAM8HxoBlNeUvBg4q918I3DNlvf8b+Fvg1l63oZ3tA/YEfrPc3w34X8CxvW5HO98/4BvAbwAB/OOAtm8v4L8Avw+cX1O+ANgG7F8e/wXwgV63o13tK8s+CJxV7u8y2dZ+vLXSvrJ8IPYvw3Sjmjz134HnlH3ft4HDeh1Xg1hfDryk9vNR/tfXlPtrgI+U+8eV/VwARwI3lPJ9gTvL333K/X3Ksrr7yEbP0cF2Hgi8pNx/OvBvwGHD1tby3E8r958C3FDivxI4oZR/EviDcv8PgU+W+ycAV5T7h5XP7e7AIeXzvOt0n+1Gz9Hh9/XdZf/2ueliGOR2AluYcmwats+tt7rve98eR2iwP+11XFNi3GHf0C834GLgd8v93YCFvY6pxLII+D6wR3l8JbCyh/HM+LtJn8R2NLCg3P9IP8VWyg+mmtz/B1OPJ/18mzc9kTLz9sz8Xp3yb2XmveXhbcBTI2J3gIh4GtWO7qzuRdqa2bYvMx/PzK+WOj8FbgYWdy/i2Zlt+yLiQGDvzPyXrP5DLwFe38WQZ2Wa9j2WmV8HfjxlUZTbXuXM5N7AvVPX7xcttA/g7cD/U+o9kZkPdDjMlrXSvkHavwyZw4HNmXln2fddDizvcUx1ZebXgAenFC+n+qJL+fv6mvJLsnI9sLDsB18NbMzMBzPzIWAjcEyTfWSj5+iIzNyamTeX+48Ct1N9aR6qtpZ4t5eHTym3BF4BXNWgnZOxXQUcVfb3y4HLM/Mnmfl9YDPV57ruZ7us0+g5OiIiFgPHA58qj6eLYWDb2cBQfW5VV98eR6bZn/aFqfuGfhERe1P9yL8Qqt9Gmflwb6PawQJgj4hYQNURoGff+Wf53aSr6sWWmV/MzIny8Hp69Hu3wesGcA7wXqrvAwNj3iSRZui3gG9l5k/K4zOBs4HHexdSW01tH1B13wReC3y5J1G1T237FgHjNcvG6aOD6Fxl5s+APwA2UR1IDqMc+IZBTVfTMyPi5oj4u4gY6WlQ7Tds+5dBsQi4u+bxoO0bRjJzK1Q/FoADSnmjdk1X3mgf2eg5Oq4MZXoxVS+doWtrGcZxC1VP0o1UvRkervmCWxvbL9pTlj8C7Mfs27/fNM/RKedSfSl+ojyeLoZBbmcCX4xqOP2qUjZ0n1vtZCCOI1P2p/1i6r6hXzwHuB/46zLU7lMRsVevgwLIzHuAjwJ3AVuBRzLzi72NaieDsk96O1Wvzr4QEa+jGkXz7V7HMltDlUSKiC+VsaJTb03PDkTEC6i6uP1eefwi4LmZ+dkOhz1j7WxfTfkC4DLg45l5Z2cin5k2t6/e/Ec9zfDOpX11tvUUqiTSi4GDgO8Ap7c55NnG1Lb2UZ1xWQz878x8CfAvVAfQnmnz+9d3+5d5pO/2DW3SqF2zLe+Z0jvvM8C7MvNH01WtUzYQbc3Mn2fmi6j2b4dTDYXdqVr52652drX9EfEaYFtm3lRbPE0MA9nO4mXlGHUscEpEvHyauoPQHs1M3783s9ifdk2DfUO/WEA11OiCzHwx8BjVsKyeK/MLLaca1nsQ1SiE3+ltVIMnIv4UmAAu7XUsUM1PDPwp8D96HUsrFvQ6gHbKzFe2sl7pWvlZ4KTM/PdS/BvAr0XEFqrX6YCIGMvM0XbE2oo2t2/SOuCOzDx3rvHNVZvbN86O3RUX0+PhXq22r4EXlW3+O0BEXEmPD3Ztbt8PqXroTCZZ/g44uY3bn7U2t6/v9i/zyDjV+PNJPd83zNJ9EXFgZm4tw122lfJG7RoHRqeUjzH9PrLRc3RMSYx/Brg0M69uEsdAtxUgMx+OiDGquXEWRsSC0oOmNrbJdo6XEz7PoOoKP91nuF75A9M8Rye8DHhdRBwHPJVquPW508QwqO0ky3D6zNwWEZ+lSgwO7edWv9DXx5EG+9N+sNO+ISI+nZn9kBAZB8Yzc7LX1lX0SRIJeCXw/cy8HyAirgZeCny6p1HtqK/3SVFdsOA1wFFleHA/+GWqxOC3q9HYLAZujojDM/M/ehrZDAxVT6RWlGEz1wKnZ+b/nizPzAsy86DMXEI1Me6/DeIPvEbtK8vOovqy9q5exNYO07x/W4FHI+LIMk/CScA1PQqzE+4BDouIZ5bHr6Ia9z4Uyg7+H3jyi/VRwHd7FlCbDcv+ZUDdCBwa1VWcdqOayHdDj2OajQ3A5NWbVvDkfm0DcFJUjqTq7r6VarLGoyNin3I282jgC032kY2eoyPK818I3J6ZH6tZNFRtjYhnlmMWEbEH1Q+D24GvAm9o0M7J2N4AfKXsG///9u4/TJKyPPT+9w4IGER2F2UO7mIWZWOEEBEnQGLevBNIAMG4nOuSZHOILmSTffOGJCaSyGJyDkYlgZwoxvjrbARdjApINOwR/LEB5hjfhB8iyE8JK2xgWQR1F3Q0kqze7x/1DPQOPV0zvdPd1TPfz3X1NVVPPVV1V093Vddd9dSzEVgV1fP/DgFWUD2Aue1nu8wz3TrmXGaem5nLyv5tVYn79Pm2nRGxb0TsNzlM9Xm7k3n2uVVbjT2OdNifDtw0+4YmJJAoJ+0PRcRLSlGTfnc+CBwbVc/aQRVb037zN3afFBEnAecAr8nMxjxCIjPvyMwDM3N5+U5spXoofuMTSMCC6p3tv1L9c54EHqU6QAP8KdUti7e1vA6cMu9yGt570my3jyrbmVQ7ocny3xz0dszl/w8YpfpB9zXgPUAMejtmu31l2haqq7ITpc5kLzS/Xf5/t1MlXA4Y9HbM8fb9GPCFsn3XAi8c9HbM5fa1TG/8/mW+vah6SfrXsm/4k0HH0yHOj1M9/+A/y2dnDdVzX64F7it/l5S6Aby3bNMd7NpL4G9QPZR4M3BmS3nbfeR06+jhdv5cOR7d3rIfP3m+bSvwU8CtZTvvBP5HKX8RVXJkM9Vdl3uX8n3K+OYy/UUty/qTsi330tLz6HSf7enW0YfP8BhP9842r7azrOsr5XXXZBzz7XPra9r/fyOPI0yzPx10XG3ifGrf0JQX1V3+Xyrv3T9Qeklswouqx+Kvlv3BR/q1D58mlhn/NmlIbJupnmE2+X34QFNimzJ9C0PUO9vkwUiSJEmSJEma1oJvziZJkiRJkqR6JpEkSZIkSZJUyySSJEmSJEmSaplEkiRJkiRJUi2TSJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTLJJIkSZIkSZJqmUSSJEmSJElSLZNIkiRJkiRJqmUSSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVMskkiRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJUyySSJEmSJEmSaplEkiRJkiRJUi2TSJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTLJJIkSZIkSZJqmUSSJEmSJElSLZNIWvAiYjwifrPLeV8YERMRscdcxyVJkiRJUpOYRJJmISK2RMQvTo5n5oOZ+ZzM/MEg45IkzVxEfDgi3l5TZywits7hOjMiDp2r5UmShsdMjjvSsDCJJEmSGmdq0n6u6kqS1I7HHWlmTCKpUcoO+dyIuDsidkTEhyJinzLttyJic0Rsj4iNEfGClvkyIn4/Iu6PiG9GxP+MiB8p094SEX/XUnd5qb9nm/W/OCKui4hvleV8NCIWlWkfAV4I/O/ShO1NU5cVES8osW0vsf5Wy7LfEhFXRMSlEfGdiLgrIkZ79V5KkoaDTaIlSVO1O1eRmsAkkprodOBE4MXAjwN/GhHHAX8B/ApwEPBvwGVT5vuvwChwFLAS+I0u1h1lPS8AXgocDLwFIDNfBzwI/HJpwvaXbeb/OLC1zP9a4M8j4viW6a8pcS8CNgLv6SJGSZrXpknav6Yk3x8vz7J76XR1S/knIuLrEfFERHwhIg7vMpY3l4sKWyLi9JbyvSPiryLiwYh4NCI+EBHPbpn+xxHxSERsi4jfmLLMD0fE+yPimoj4LvALEbF/ucjwjYj4t4j405aLIT9Sxv8tIh4r9fYv0yYvZpwZEQ+VCzC/HRE/HRG3l/frPS3rPjQi/k95X74ZEZd3875I0nzShONOlGbUEXFORHwd+FAp73Qh/Wcj4uayzpsj4mdbpo1HxNsj4p9LnP87Ig4oF8m/XeovL3UjIi4qx5gnyvHjJ3frTdW8ZRJJTfSezHwoM7cD5wO/RpVYuiQzv5yZTwLnAj8zueMrLszM7Zn5IPCuMt+sZObmzNyUmU9m5jeAdwL/90zmjYiDgZ8DzsnM72fmbcAHgde1VPtiZl5TnqH0EeBls41Rkua7qUl74B+okvR/ADwfuIbqx/teHRL8nwFWAAcCXwY+2kUo/wV4HrAUWA2sj4iXlGkXUl3oOBI4tNT5HwARcRLwR8AvlRjaNXn4b1THuP2ALwJ/A+wPvIjquPN64MxS94zy+oUy/Tk88yLEMWVdv0p1DPyTst7DgV+JiMlj2duAzwOLgWVlvZK0oDXsuLME+DFgbacL6RGxBLgaeDdwANV5y9URcUDL8lZRnYsspbpA/y9UyaklwD3AeaXeCcDPUx3XFlEdS77VRfxaAEwiqYkeahn+N6q7el5QhgHIzAmqHdvSmvlmJSIOjIjLIuLhiPg28HdUJxAz8QJge2Z+Z0ocrTF+vWX4e8A+4a2qklTnV4GrS5L/P4G/Ap4N/Ox0M2TmJZn5nXLh4S3Ayybv3pml/14uLPwfqh/rvxIRAfwW8Ifl4sV3gD+n+rEO1Y/9D2XmnZn53bL+qa7KzP8vM38I/GfZxnNLzFuAd/D0RYjTgXdm5v3l+HcusGrK8eNt5QLG54HvAh/PzMcy82Hgn4CXl3r/SXVy8oJS/4tdvCeSNN8N6rjzQ+C8ctz5dzpfSD8FuC8zP5KZOzPz48BXgV9uWd6HMvNrmfkEVZLra5n5j5m5E/gEux4b9gN+AojMvCczH5ll7FogTCKpiQ5uGX4hsK28fmyyMCL2pcq4P1wzH1Q/pn+0Zdp/6bDuvwAS+KnMfC7w61RN3CZlh3m3AUsiYr8pcTw8TX1J0sxMvZDwQ6oLB0vbVY6IPSLigoj4WrkgsKVMmulFgUk7ShJo0uQFiudTHVduKc0cHgc+W8on4516YWOq1unPA/aaUq/1IsQL2kzbExhpKXu0Zfjf24w/pwy/ieq4dlNpptFN029Jmu8Gddz5RmZ+v0McrRfSpx4b4JkXsGd0bMjM66jucH0v8GhErI+I584ydi0QJpHURGdFxLJyi+abgcuBjwFnRsSREbE31RXfG8vV2kl/HBGLS7OyN5T5AG4Dfj4iXliuBpzbYd37ARPA4xGxFPjjKdMfpWpK8AyZ+RDwz8BfRMQ+EfFTwBq6u5VVkha61qT91AsJQXXh4OE2daFqKraSqjnX/sDyyVlnGcPictFi0uQFim9S/fg+PDMXldf+pQkEwCM888LGVK0xf5On7xBqnWdy+7a1mbaTXU8GZiQzv56Zv5WZLwD+H+B9EXHobJcjSfNQE447U5fb6UL61GMD7MYF7Mx8d2a+gqoZ9I/zzPMgCTCJpGb6GNXzGu4vr7dn5rXAfwf+nurH+Yt5utnApKuAW6iSRlcDFwNk5iaqhNLtZfqnO6z7z6gezP1EWcYnp0z/C6oHfT8eEX/UZv5fozpobAM+RXU76qbaLZYkTdWatL8COCUijo+IZwFnA09SJe6n1oXqgsCTVFdrf5TqwkO3/iwi9oqI/wt4NfCJckX6b4GLIuJAgIhYGhEntsR7RkQcFhE/ytPPnGirPCfvCuD8iNgvIn4MeCNVk2qonsvxhxFxSEQ8p2zP5aU5wqxExGkRsayM7qA6YfnBbJcjSfNQU447rTpdSL8G+PGI+G8RsWdE/CpwGJ3PddqKqjOGY8q2fhf4Ph4bNA2TSGqimzPzsHJld3Vmfg8gMz+QmS/OzCWZ+erM3Dplvmsy80WZeUBmnl1+lFPmPass79DM/NvMjMkf35k5lpkfLMN3ZeYrykPyjszMd2TmspblXJWZLyzL+qvM3DJlWVtLbEtKrB9omfctmfnrLeO7zCtJ2sVTSXuq5zv8OtVDoL9Zxn85M/9jat2S4L+U6pb+h4G7gRu6jOHrVImWbVR3lf52Zn61TDsH2AzcUJou/CPwEoDM/AzVw62vK3Wum8G6fo/qh/v9VA/a/hhwSZl2CVVnDF8AHqD6cf97XW7TTwM3RsQEVS+hb8jMB7pcliTNJ0047uyi04X0zPwW1cWNs6mSV28CXp2Z3+xiVc+lujiyg2o7vkX1HCjpGSKz0yNepP6KiC3Ab2bmP85yvgRWZObmngQmSZIkSdIC551IkiRJkiRJqmUSSY2SmctnexdSmS+8C0mSNBsR8eaImGjz+sygY5MkzT8edzQf2JxNkiRJkiRJtfYcdACdPO95z8vly5d3Ne93v/td9t133/qKA9Dk2KDZ8Rlb95ocX5Njg7mN75ZbbvlmZj5/ThY2ZMozz75D1dvHzswcjYglVL0nLge2AL+SmTtKV7p/DZwMfA84IzO/XJazGvjTsti3Z+aGTuvt9ljS9M/lVMbbW8MU7zDFCt+Hn+gAACAASURBVMbbjYV8LBmU+XJe0qRYwHjqGE9nTYqnSbHAzOLp+liSmY19veIVr8huXX/99V3P22tNji2z2fEZW/eaHF+TY8uc2/iAL2UD9q+DeFEliZ43pewvgXVleB1wYRk+GfgMEMCxVN3ZAiyh6r1qCbC4DC/utN5ujyVN/1xOZby9NUzxDlOsmcbbjYV8LBnUa76clzQplkzjqWM8nTUpnibFkjmzeLo9lvhMJEnSIK0EJu8k2gCc2lJ+aTnG3QAsioiDgBOBTZm5PTN3AJuAk/odtCRJkrQQNbo5myRpXkng8xGRwP/KzPXASGY+ApCZj0TEgaXuUuChlnm3lrLpyncREWuBtQAjIyOMj4/POtiJiYmu5hsU4+2tYYp3mGIF45UkaZiYRJIk9csrM3NbSRRtioivdqgbbcqyQ/muBVWCaj3A6Ohojo2NzTrY8fFxuplvUIy3t4Yp3mGKFYxXkqRhYnM2SVJfZOa28vcx4FPA0cCjpZka5e9jpfpW4OCW2ZcB2zqUS5IkSeoxk0iSpJ6LiH0jYr/JYeAE4E5gI7C6VFsNXFWGNwKvj8qxwBOl2dvngBMiYnFELC7L+VwfN0WSJElasGzOJknqhxHgUxEB1bHnY5n52Yi4GbgiItYADwKnlfrXUPXQthn4HnAmQGZuj4i3ATeXem/NzO392wxJkiRp4TKJJEnqucy8H3hZm/JvAce3KU/grGmWdQlwyVzHKEmSJKkzm7NJkiRJkiSplkkkSZIkSZIk1VpQzdmWr7u6bfmWC07pcySSpPmm3THG44skaTY8lkhqOu9EkiRJkiRJUi2TSJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTLJJIkSZIkSZJqmUSSJEmSJElSLZNIkiRJkiRJqmUSSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVMskkiRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtWaURIqIP4yIuyLizoj4eETsExGHRMSNEXFfRFweEXuVunuX8c1l+vKW5Zxbyu+NiBN7s0mSJEmSJEmaa7VJpIhYCvw+MJqZPwnsAawCLgQuyswVwA5gTZllDbAjMw8FLir1iIjDynyHAycB74uIPeZ2cyRJkiRJktQLM23Otifw7IjYE/hR4BHgOODKMn0DcGoZXlnGKdOPj4go5Zdl5pOZ+QCwGTh69zdBkiRJ0kIQEVsi4o6IuC0ivlTKlkTEptJCYlNELC7lERHvLi0hbo+Io1qWs7rUvy8iVg9qeyRp2OxZVyEzH46IvwIeBP4d+DxwC/B4Zu4s1bYCS8vwUuChMu/OiHgCOKCU39Cy6NZ5nhIRa4G1ACMjI4yPj89+q4CJiYlnzHv2ETvb1u12Hd1qF1uTNDk+Y+tek+NrcmzQ/PgkSVpgfiEzv9kyvg64NjMviIh1Zfwc4FXAivI6Bng/cExELAHOA0aBBG6JiI2ZuaOfGyFJw6g2iVQy+SuBQ4DHgU9Q7ZCnyslZppk2XfmuBZnrgfUAo6OjOTY2VhdiW+Pj40yd94x1V7etu+X07tbRrXaxNUmT4zO27jU5vibHBs2PT5KkBW4lMFaGNwDjVEmklcClmZnADRGxKCIOKnU3ZeZ2gIjYRPW4jY/3N2xJGj61SSTgF4EHMvMbABHxSeBngUURsWe5G2kZsK3U3wocDGwtzd/2B7a3lE9qnUeSJEmS6iTw+YhI4H+VC9AjmfkIQGY+EhEHlrpPtZAoJltCTFe+i162kJhOu5YTc3k3dNPurjaezoynsybF06RYoLfxzCSJ9CBwbET8KFVztuOBLwHXA68FLgNWA1eV+hvL+L+U6ddlZkbERuBjEfFO4AVUt5XeNIfbIkmSJGl+e2VmbiuJok0R8dUOdRvbQmI67VpOzGWriabdXW08nRlPZ02Kp0mxQG/jqX2wdmbeSPWA7C8Dd5R51lPdIvrGiNhM9cyji8ssFwMHlPI3UrVJJjPvAq4A7gY+C5yVmT+Y062RJEmSNG9l5rby9zHgU1Qd9TxamqlR/j5Wqk/XEsIWEpLUpRn1zpaZ52XmT2TmT2bm60oPa/dn5tGZeWhmnpaZT5a63y/jh5bp97cs5/zMfHFmviQzP9OrjZIkSZI0v0TEvhGx3+QwcAJwJ0+3hIBntpB4feml7VjgidLs7XPACRGxuDz/9YRSJkmqMZPmbJIkSZI0aCPApyICqvOYj2XmZyPiZuCKiFhD9SiO00r9a4CTgc3A94AzATJze0S8Dbi51Hvr5EO2JUmdmUSSJEmS1HilhcPL2pR/i+q5rVPLEzhrmmVdAlwy1zFK0nxnEkmSpB5Z3uYBqQBbLjilz5FIkiRJu29Gz0SSJEmSJEnSwmYSSZLUNxGxR0TcGhGfLuOHRMSNEXFfRFweEXuV8r3L+OYyfXnLMs4t5fdGxImD2RJJkiRp4TGJJEnqpzcA97SMXwhclJkrgB3AmlK+BtiRmYcCF5V6RMRhwCrgcOAk4H0RsUefYpckSZIWNJNIkqS+iIhlwCnAB8t4AMcBV5YqG4BTy/DKMk6ZfnypvxK4LDOfzMwHqHrcObo/WyBJkiQtbCaRJEn98i7gTcAPy/gBwOOZubOMbwWWluGlwEMAZfoTpf5T5W3mkSRJktRD9s4mSeq5iHg18Fhm3hIRY5PFbapmzbRO87Suby2wFmBkZITx8fHZhszExMSs5jv7iJ31lYpu4qkz23gHzXh7Z5hiBeOVJGmYmESSJPXDK4HXRMTJwD7Ac6nuTFoUEXuWu42WAdtK/a3AwcDWiNgT2B/Y3lI+qXWep2TmemA9wOjoaI6Njc064PHxcWYz3xnrrp5x3S2nzz6eOrONd9CMt3eGKVYwXi1My2dxzJCkJrE5mySp5zLz3MxclpnLqR6MfV1mng5cD7y2VFsNXFWGN5ZxyvTrMjNL+arSe9shwArgpj5thiRJkrSgeSeSJGmQzgEui4i3A7cCF5fyi4GPRMRmqjuQVgFk5l0RcQVwN7ATOCszf9D/sCVJkqSFxySSJKmvMnMcGC/D99Omd7XM/D5w2jTznw+c37sIJUmSJLVjczZJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVMskkiRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJUyySSJEmSJEmSaplEkiRJkiRJUi2TSJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiQNjYjYIyJujYhPl/FDIuLGiLgvIi6PiL1K+d5lfHOZvrxlGeeW8nsj4sTBbIkkDR+TSJIkSZKGyRuAe1rGLwQuyswVwA5gTSlfA+zIzEOBi0o9IuIwYBVwOHAS8L6I2KNPsUvSUDOJJEmSJGkoRMQy4BTgg2U8gOOAK0uVDcCpZXhlGadMP77UXwlclplPZuYDwGbg6P5sgSQNtz0HHYAkSZIkzdC7gDcB+5XxA4DHM3NnGd8KLC3DS4GHADJzZ0Q8UeovBW5oWWbrPE+JiLXAWoCRkRHGx8e7CnhiYuIZ8559xM72ldvodr0zjWWQjKcz4+msSfE0KRbobTwmkSRJkiQ1XkS8GngsM2+JiLHJ4jZVs2Zap3meLshcD6wHGB0dzbGxsalVZmR8fJyp856x7uoZz7/l9O7WO9NYBsl4OjOezpoUT5Nigd7GYxJJkiRJ0jB4JfCaiDgZ2Ad4LtWdSYsiYs9yN9IyYFupvxU4GNgaEXsC+wPbW8ontc4jSerAZyJJkiRJarzMPDczl2XmcqoHY1+XmacD1wOvLdVWA1eV4Y1lnDL9uszMUr6q9N52CLACuKlPmyFJQ807kSRJkiQNs3OAyyLi7cCtwMWl/GLgIxGxmeoOpFUAmXlXRFwB3A3sBM7KzB/0P2xJGj4zuhMpIhZFxJUR8dWIuCcifiYilkTEpoi4r/xdXOpGRLw7IjZHxO0RcVTLclaX+vdFxOrp1yhJkiRJ7WXmeGa+ugzfn5lHZ+ahmXlaZj5Zyr9fxg8t0+9vmf/8zHxxZr4kMz8zqO2QpGEz0+Zsfw18NjN/AngZcA+wDrg2M1cA15ZxgFdR3RK6gqo3g/cDRMQS4DzgGKouNM+bTDxJkiRJkiSp2WqTSBHxXODnKbeFZuZ/ZObjwEpgQ6m2ATi1DK8ELs3KDVQPujsIOBHYlJnbM3MHsAk4aU63RpIkSZIkST0xkzuRXgR8A/hQRNwaER+MiH2Bkcx8BKD8PbDUXwo81DL/1lI2XbkkSZIkSZIabiYP1t4TOAr4vcy8MSL+mqebrrUTbcqyQ/muM0espWoGx8jICOPj4zMI8ZkmJiaeMe/ZR+xsW7fbdXSrXWxN0uT4jK17TY6vybFB8+OTJEmSpH6YSRJpK7A1M28s41dSJZEejYiDMvOR0lztsZb6B7fMvwzYVsrHppSPT11ZZq4H1gOMjo7m2NjY1CozMj4+ztR5z1h3ddu6W07vbh3dahdbkzQ5PmPrXpPja3Js0Pz4JEmSJKkfapuzZebXgYci4iWl6Hiq7jA3ApM9rK0GrirDG4HXl17ajgWeKM3dPgecEBGLywO1TyhlkiRJkiRJariZ3IkE8HvARyNiL+B+4EyqBNQVEbEGeBA4rdS9BjgZ2Ax8r9QlM7dHxNuAm0u9t2bm9jnZCkmSJEmSJPXUjJJImXkbMNpm0vFt6iZw1jTLuQS4ZDYBSpIkSdJCtXy6R3JccEqfI5GkmfXOJkmSJEmSpAXOJJIkqeciYp+IuCkivhIRd0XEn5XyQyLixoi4LyIuL82miYi9y/jmMn15y7LOLeX3RsSJg9kiSZIkaeExiSRJ6ocngeMy82XAkcBJpfOFC4GLMnMFsANYU+qvAXZk5qHARaUeEXEYsAo4HDgJeF9E7NHXLZEkSZIWKJNIkqSey8pEGX1WeSVwHHBlKd8AnFqGV5ZxyvTjIyJK+WWZ+WRmPkDVicPRfdgESZIkacGbae9skiTtlnLH0C3AocB7ga8Bj2fmzlJlK7C0DC8FHgLIzJ0R8QRwQCm/oWWxrfO0rmstsBZgZGSE8fHxWcc7MTExq/nOPmJnfaWim3jqzDbeQTPe3hmmWMF4JUkaJiaRJEl9kZk/AI6MiEXAp4CXtqtW/sY006Yrn7qu9cB6gNHR0RwbG5t1vOPj48xmvjOm6T2nnS2nzz6eOrONd9CMt3eGKVYwXkmShonN2SRJfZWZjwPjwLHAooiYvKCxDNhWhrcCBwOU6fsD21vL28wjSZIkqYdMIkmSei4inl/uQCIing38InAPcD3w2lJtNXBVGd5YxinTr8vMLOWrSu9thwArgJv6sxWSJEnSwmZzNklSPxwEbCjPRfoR4IrM/HRE3A1cFhFvB24FLi71LwY+EhGbqe5AWgWQmXdFxBXA3cBO4KzSTE6SJElSj5lEkiT1XGbeDry8Tfn9tOldLTO/D5w2zbLOB86f6xglSZIkdWZzNkmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJUyySSJEmSJEmSavlgbUmSZmH5uqsHHYIkSZI0EN6JJEmSJEmSpFomkSRJkiRJklTLJJIkSZIkSZJqmUSSJEmSJElSLR+sLUlSn033cO4tF5zS50gkSZKkmfNOJEmSJEmSJNUyiSRJkiSp8SJin4i4KSK+EhF3RcSflfJDIuLGiLgvIi6PiL1K+d5lfHOZvrxlWeeW8nsj4sTBbJEkDR+TSJIkSZKGwZPAcZn5MuBI4KSIOBa4ELgoM1cAO4A1pf4aYEdmHgpcVOoREYcBq4DDgZOA90XEHn3dEkkaUiaRJEmSJDVeVibK6LPKK4HjgCtL+Qbg1DK8soxTph8fEVHKL8vMJzPzAWAzcHQfNkGShp4P1pYkSZI0FModQ7cAhwLvBb4GPJ6ZO0uVrcDSMrwUeAggM3dGxBPAAaX8hpbFts7Tuq61wFqAkZERxsfHu4p5YmLiGfOefcTO9pVnoZt42sUySMbTmfF01qR4mhQL9DYek0iSJEmShkJm/gA4MiIWAZ8CXtquWvkb00ybrnzqutYD6wFGR0dzbGysm5AZHx9n6rxnTNNL52xsOX328bSLZZCMpzPj6axJ8TQpFuhtPDZnkyRJkjRUMvNxYBw4FlgUEZMXx5cB28rwVuBggDJ9f2B7a3mbeSRJHZhEkiRJktR4EfH8cgcSEfFs4BeBe4DrgdeWaquBq8rwxjJOmX5dZmYpX1V6bzsEWAHc1J+tkKThZnM2SZIkScPgIGBDeS7SjwBXZOanI+Ju4LKIeDtwK3BxqX8x8JGI2Ex1B9IqgMy8KyKuAO4GdgJnlWZykqQaJpEkSZIkNV5m3g68vE35/bTpXS0zvw+cNs2yzgfOn+sYJWm+szmbJEmSJEmSaplEkiRJkiRJUi2TSJIkSZIkSaplEkmSJEmSJEm1fLA2sHzd1c8o23LBKQOIRJIkSZIkqZm8E0mSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1Zq3z0S64+EnOKPNs44kSZIkSZI0ezO+Eyki9oiIWyPi02X8kIi4MSLui4jLI2KvUr53Gd9cpi9vWca5pfzeiDhxrjdGkiRJkiRJvTGb5mxvAO5pGb8QuCgzVwA7gDWlfA2wIzMPBS4q9YiIw4BVwOHAScD7ImKP3QtfkiRJkiRJ/TCjJFJELANOAT5YxgM4DriyVNkAnFqGV5ZxyvTjS/2VwGWZ+WRmPgBsBo6ei42QJEmSJElSb830TqR3AW8CfljGDwAez8ydZXwrsLQMLwUeAijTnyj1nypvM48kSZIkSZIarPbB2hHxauCxzLwlIsYmi9tUzZppneZpXd9aYC3AyMgI4+PjdSG2NfJsOPuInfUVp9HtemdiYmKip8vfXU2Oz9i61+T4mhwbND8+SZIkSeqHmfTO9krgNRFxMrAP8FyqO5MWRcSe5W6jZcC2Un8rcDCwNSL2BPYHtreUT2qd5ymZuR5YDzA6OppjY2NdbBb8zUev4h13dN/53JbTu1vvTIyPj9PtdvVDk+Mztu41Ob4mxwbNj0+SJEmS+qG2OVtmnpuZyzJzOdWDsa/LzNOB64HXlmqrgavK8MYyTpl+XWZmKV9Vem87BFgB3DRnWyJJkiRJkqSemU3vbFOdA7wxIjZTPfPo4lJ+MXBAKX8jsA4gM+8CrgDuBj4LnJWZP9iN9UuShkREHBwR10fEPRFxV0S8oZQviYhNEXFf+bu4lEdEvDsiNkfE7RFxVMuyVpf690XE6unWKUmSJGluzaq9V2aOA+Nl+H7a9K6Wmd8HTptm/vOB82cbpCRp6O0Ezs7ML0fEfsAtEbEJOAO4NjMviIh1VBcezgFeRXXH6grgGOD9wDERsQQ4Dxileq7eLRGxMTN39H2LJEmSpAVmd+5EkiRpRjLzkcz8chn+DnAPVQ+dK4ENpdoG4NQyvBK4NCs3UD2H7yDgRGBTZm4viaNNwEl93BRJkiRpwer+ydOSJHUhIpYDLwduBEYy8xGoEk0RcWCpthR4qGW2raVsuvKp69jtnj6n65Vvd3r+rLM7vQAOWy+Cxts7wxQrGK8kScPEJJIkqW8i4jnA3wN/kJnfjohpq7Ypyw7luxbMQU+f0/XKd8a6q2e9rJnanZ5Bh60XQePtnWGKFYxXkqRhYnM2SVJfRMSzqBJIH83MT5biR0szNcrfx0r5VuDgltmXAds6lEuSJEnqMZNIkqSei+qWo4uBezLznS2TNgKTPaytBq5qKX996aXtWOCJ0uztc8AJEbG49OR2QimTJEmS1GM2Z5Mk9cMrgdcBd0TEbaXszcAFwBURsQZ4kKd797wGOBnYDHwPOBMgM7dHxNuAm0u9t2bm9v5sgiRJkrSwmUSSJPVcZn6R9s8zAji+Tf0EzppmWZcAl8xddJIkSZJmwuZskiRJkiRJqmUSSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkqfEi4uCIuD4i7omIuyLiDaV8SURsioj7yt/FpTwi4t0RsTkibo+Io1qWtbrUvy8iVg9qmyRp2JhEkiRJkjQMdgJnZ+ZLgWOBsyLiMGAdcG1mrgCuLeMArwJWlNda4P1QJZ2A84BjgKOB8yYTT5KkzvYcdACSJEmSVCczHwEeKcPfiYh7gKXASmCsVNsAjAPnlPJLMzOBGyJiUUQcVOpuysztABGxCTgJ+HjfNmYOLF93ddvyLRec0udIJC0kJpEkSWqIdicEngxI0jNFxHLg5cCNwEhJMJGZj0TEgaXaUuChltm2lrLpyqeuYy3VHUyMjIwwPj7eVawTExPPmPfsI3Z2tayZ6BRnu1gGyXg6M57OmhRPk2KB3sZjEkmSJEnS0IiI5wB/D/xBZn47Iqat2qYsO5TvWpC5HlgPMDo6mmNjY13FOz4+ztR5z5jmLqK5sOX0sWmntYtlkIynM+PprEnxNCkW6G08PhNJkiRJ0lCIiGdRJZA+mpmfLMWPlmZqlL+PlfKtwMEtsy8DtnUolyTVMIkkSZIkqfGiuuXoYuCezHxny6SNwGQPa6uBq1rKX196aTsWeKI0e/sccEJELC4P1D6hlEmSaticTZIkSdIweCXwOuCOiLitlL0ZuAC4IiLWAA8Cp5Vp1wAnA5uB7wFnAmTm9oh4G3BzqffWyYdsS5I6M4kkSZIkqfEy84u0f54RwPFt6idw1jTLugS4ZO6ik6SFweZskiRJkiRJqmUSSZIkSZIkSbVMIkmSJEmSJKmWSSRJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVMskkiRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJUa89BByBJkiRJ89UdDz/BGeuuHnQYkjQnvBNJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRaJpEkSZIkSZJUqzaJFBEHR8T1EXFPRNwVEW8o5UsiYlNE3Ff+Li7lERHvjojNEXF7RBzVsqzVpf59EbG6d5slSZIkSZKkuTSTO5F2Amdn5kuBY4GzIuIwYB1wbWauAK4t4wCvAlaU11rg/VAlnYDzgGOAo4HzJhNPkiRJkiRJarbaJFJmPpKZXy7D3wHuAZYCK4ENpdoG4NQyvBK4NCs3AIsi4iDgRGBTZm7PzB3AJuCkOd0aSZIkSZIk9cSes6kcEcuBlwM3AiOZ+QhUiaaIOLBUWwo81DLb1lI2XfnUdayluoOJkZERxsfHZxPiU0aeDWcfsbOreYGu1zsTExMTPV3+7mpyfMbWvSbH1+TYoPnxDYOIuAR4NfBYZv5kKVsCXA4sB7YAv5KZOyIigL8GTga+B5wxeTGjNIX+07LYt2fmBua55euublu+5YJT+hyJJEmSFroZJ5Ei4jnA3wN/kJnfrn7jt6/apiw7lO9akLkeWA8wOjqaY2NjMw1xF3/z0at4xx2zypHtYsvp3a13JsbHx+l2u/qhyfEZW/eaHF+TY4PmxzckPgy8B7i0pWyyWfQFEbGujJ/Drs2ij6FqFn1MS7PoUarjxy0RsbHc3SpJkiSpx2bUO1tEPIsqgfTRzPxkKX60NFOj/H2slG8FDm6ZfRmwrUO5JGmey8wvANunFNssWpIkSRoitbfqlGYFFwP3ZOY7WyZtBFYDF5S/V7WU/25EXEZ1BfmJ0tztc8CftzxM+wTg3LnZDEnSEOpJs2iYm6bR0zVj3J2m0nNpamzD1uzSeHtnmGIF45UkaZjMpL3XK4HXAXdExG2l7M1UyaMrImIN8CBwWpl2DdVzLDZTPcviTIDM3B4RbwNuLvXemplTr0pLkrRbzaJhbppGT9eM8YxpnlHUb1ObXQ9bs0vj7Z1hihWMV5KkYVKbRMrML9L+hzvA8W3qJ3DWNMu6BLhkNgFKkuatRyPioHIX0kybRY9NKR/vQ5ySJA2Ndh0y2BmDpLnS/ZOnJUnaPY1vFj1dz2iSpP6zp09JGrwZPVhbkqTdEREfB/4FeElEbC1NoS8Afiki7gN+qYxD1Sz6fqpm0X8L/A5UzaKByWbRN2OzaElaaD7MMztUmOzpcwVwbRmHXXv6XEvV0yctPX0eAxwNnNdycUKSVMM7kSRJPZeZvzbNJJtFS5JmJDO/EBHLpxSv5OmmzhuomjmfQ0tPn8ANETHZ0+cYpadPgIiY7Onz4z0OX5LmBe9EkiRJkjSsdunpE5iznj4lSc/knUjTmO45GD6UTpIkSWq83e7pMyLWUjWFY2RkhPHx8a4CGXk2nH3Ezq7mnSuTsU9MTHS9Hb1gPJ0ZT2dNiqdJsUBv4zGJJEmSJGlY9aynz8xcD6wHGB0dzbGxsXbVav3NR6/iHXcM9rRry+ljQJVM6nY7esF4OjOezpoUT5Nigd7GY3M2SZIkScNqsqdPeGZPn6+PyrGUnj6BzwEnRMTi8kDtE0qZJGkGvBNJkiRJUuOVnj7HgOdFxFaqXtYuAK4ovX4+CJxWql8DnEzV0+f3gDOh6ukzIiZ7+gR7+pSkWTGJJEmSJKnx7OlTkgbP5mySJEmSJEmqZRJJkiRJkiRJtUwiSZIkSZIkqZZJJEmSJEmSJNUyiSRJkiRJkqRa9s4mSdIQWr7u6l3Gzz5iJ2esu5otF5wyoIgkSZI033knkiRJkiRJkmqZRJIkSZIkSVItk0iSJEmSJEmqZRJJkiRJkiRJtXywtiRJkiTNY5OdMUx2wjDJzhgkzZZ3IkmSJEmSJKmWSSRJkiRJkiTVsjmbJEnzyPKWZgqTbK4gSZKkueCdSJIkSZIkSaplEkmSYK3DBAAACxZJREFUJEmSJEm1bM42S+2aCYBNBSRJkiRJ0vxmEkmSJEmSFiAvkEuaLZNIkiTNc54kSJIkaS74TCRJkiRJkiTVMokkSZIkSZKkWiaRJEmSJEmSVMtnIkmStED5rCRJUjvtjg8eGySBdyJJkiRJkiRpBkwiSZIkSZIkqZbN2eaIt3xKkiRJkqT5zCSSJEnahRdGJEmS1I5JJEmSVMuHcEvSwuZxQBKYROqp6Xa0Hz5p3z5HIkmSJElzz+SStLCYRJIkSV2z6ZskSdLC0fckUkScBPw1sAfwwcy8oN8xSJKGm8eSZpvuqnSrs4/YyRmlnkknSYPgsaS3ZnMs8DggDY++JpEiYg/gvcAvAVuBmyNiY2be3c84Bu2Oh5946odzHXeokrQrjyXzz0xONCZNd1z0jihJs+GxpFnch0vDo993Ih0NbM7M+wEi4jJgJeDOehqz+WE9F9xZSxoCHksWsNkcF+fyGNp65xS0P176XBBpqHgsabh+nwfBM/f1vebxQcOo30mkpcBDLeNbgWNaK0TEWmBtGZ2IiHu7XNfzgG92OW9P/X6DY4sLgQbHh7HtjibH1+TYYG7j+7E5Ws5C1q9jSdM/l7to8rGlnWGPtxwvZ2Q2defIUL23GG83PJbsvgV5XtK0fe9Cj2cGx4dGvT8YTydNigVmFk9Xx5J+J5GiTVnuMpK5Hli/2yuK+FJmju7ucnqhybFBs+Mztu41Ob4mxwbNj28B6suxZNj+78bbW8MU7zDFCsargVmQ5yVNigWMp47xdNakeJoUC/Q2nh/pxUI72Aoc3DK+DNjW5xgkScPNY4kkaXd5LJGkLvQ7iXQzsCIiDomIvYBVwMY+xyBJGm4eSyRJu8tjiSR1oa/N2TJzZ0T8LvA5qq40L8nMu3q0ut2+9bSHmhwbNDs+Y+tek+NrcmzQ/PgWlD4eS4bt/268vTVM8Q5TrGC8GoAFfF7SpFjAeOoYT2dNiqdJsUAP44nMrK8lSZIkSZKkBa3fzdkkSZIkSZI0hEwiSZIkSZIkqda8SyJFxEkRcW9EbI6IdX1c75aIuCMibouIL5WyJRGxKSLuK38Xl/KIiHeXGG+PiKNalrO61L8vIlbvRjyXRMRjEXFnS9mcxRMRryjbu7nM266b1NnE9paIeLi8f7dFxMkt084t67k3Ik5sKW/7vy4PSLyxxHx5eVjibN67gyPi+oi4JyLuiog3NOX96xBbI96/iNgnIm6KiK+U+P6s0zIjYu8yvrlMX95t3LsR24cj4oGW9+7IUt7X74WapdvPWQ/i6Om+fI5j7fm+c47j7fn+qgcx7xERt0bEp4cg1kb9LppBvIsi4sqI+Gr5DP9Mk+PVcOjnsWTQ37lo0LnHNLEM7LdyNOzcokM8A3mPokHnDx1iGej5QvTw+D/T92YXmTlvXlQPxfsa8CJgL+ArwGF9WvcW4HlTyv4SWFeG1wEXluGTgc8AARwL3FjKlwD3l7+Ly/DiLuP5eeAo4M5exAPcBPxMmeczwKt2M7a3AH/Upu5h5f+4N3BI+f/u0el/DVwBrCrDHwD+31m+dwcBR5Xh/YB/LXEM/P3rEFsj3r+yPc8pw88CbizvSdtlAr8DfKAMrwIu7zbu3Yjtw8Br29Tv6/fCV3Neu/M560EsPd2Xz3GsPd93znG8Pd1f9ejz8EbgY8Cny3iTY91Cg34XzSDeDcBvluG9gEVNjtdX81/0+Vgy6O8cDTr3mCaWtzCg38o07NyiQzwDeY9o0PlDh1g+zADPF+jR8X82703ra77diXQ0sDkz78/M/wAuA1YOMJ6VVD9KKH9PbSm/NCs3AIsi4iDgRGBTZm7PzB3AJuCkblacmV8AtvcinjLtuZn5L1l9Ki9tWVa3sU1nJXBZZj6ZmQ8Am6n+z23/1yWTexxwZZvtnGl8j2Tml8vwd4B7gKU04P3rENt0+vr+lfdgoow+q7yywzJb39MrgeNLDLOKezdjm05fvxdqlMYcS3q5L+9BrD3dd/Yg3l7vr+ZURCwDTgE+WMY77a8HGmsHjfwsRMRzqU46LwbIzP/IzMebGq+GRhOOJX37DDfp3KNp5xpNO7do2vlEk84fmni+0OPjf1f7qfmWRFoKPNQyvpXOX4i5lMDnI+KWiFhbykYy8xGovqzAgTVx9jr+uYpnaRme6zh/t9wGeEmU2zm7iO0A4PHM3DkXsZVbAF9OlYVu1Ps3JTZoyPtXbre8DXiMaof5tQ7LfCqOMv2JEkNPviNTY8vMyffu/PLeXRQRe0+NbYYx9Op7of4b5LFkJpp2bHmGHu07exFnL/dXc+1dwJuAH5bxTvvrQccKw/G7aNKLgG8AHyrNBT4YEfs2OF4Nh35/Hpr4nWvUb2ca8Fu5aecWTTmfaNL5QwPPF3p5/O/qOz/fkkjt2hN2yhzOpVdm5lHAq4CzIuLnO9SdLs5BxT/beHoR5/uBFwNHAo8A7xh0bBHxHODvgT/IzG93qjrLWHY7xjaxNeb9y8wfZOaRwDKq7PZLOyyzr/FNjS0ifhI4F/gJ4Kepbjk9ZxCxqVGG9X/ZiM9mD/edc67H+6s5ExGvBh7LzFtaizusd+DvLcP1u2hPqqYv78/MlwPfpWpaMp1Bx6vh0O/PwzB95wbxG2vgv5Wbdm7RpPOJJp0/NOl8oQ/H/64+O/MtibQVOLhlfBmwrR8rzsxt5e9jwKeoPvyPllvWKH8fq4mz1/HPVTxby/CcxZmZj5Yv7A+Bv+Xp2+tnG9s3qW4j3HN3YouIZ1HtVD+amZ8sxY14/9rF1rT3r8T0ODBO1T54umU+FUeZvj/V7cc9/Y60xHZSuaU3M/NJ4EN0/97N+fdCAzOwY8kMNe3Y8pQe7zt7pkf7q7n0SuA1EbGF6lbz46iuTDYxVmBofhdN2gpsbbnafCVVUqmp8Wo49PXz0NDvXCN+O8Pgfys37dyiqecTTTp/aMj5Qq+P/91953OOHubWhBfVlaT7qR4WNflgqMP7sN59gf1ahv+Zqv3w/2TXh6X9ZRk+hV0fwHVTPv0ArgeoHr61uAwv2Y24lrPrA+XmLB7g5lJ38oFgJ+9mbAe1DP8hVZtNgMPZ9SFg91M9AGza/zXwCXZ90NjvzDK2oGqf+q4p5QN//zrE1oj3D3g+sKgMPxv4J+DV0y0TOItdH/52Rbdx70ZsB7W8t+8CLhjU98JXM1678znrUTzL6dG+fI7j7Pm+c47j7en+qoefhzGefrBmI2Olob+LamL+J+AlZfgtJdbGxuur+S/6eCxpyneOBp17tIllYL+Vadi5RYd4BvIe0aDzhw6xDPx8gR4c/2fz3uwSSy92ZIN8UT0h/V+p2lH+SZ/W+aLyhn8FuGtyvVTtD68F7it/Jz84Aby3xHgHMNqyrN+getDVZuDM3Yjp41S3If4nVYZxzVzGA4wCd5Z53gPEbsb2kbLu24GN7LoT+5OynntpeXr9dP/r8v+4qcT8CWDvWb53P0d1G9/twG3ldXIT3r8OsTXi/QN+Cri1xHEn8D86LRPYp4xvLtNf1G3cuxHbdeW9uxP4O57ukaGv3wtfzXp1+znrQRw93ZfPcaw933fOcbw931/1KO4xnv4R2chYaeDvohnEfCTwpfJ5+AeqH/2NjdfXcLzo07GkCd85GnTuMU0sA/utTMPOLTrEM5D3iAadP3SIZeDnC/To+D/T96b1FWVGSZIkSZIkaVrz7ZlIkiRJkiRJ6gGTSJIkSZIkSaplEkmSJEmSJEm1TCJJkiRJkiSplkkkSZIkSZIk1TKJJEmSJEmSpFomkSRJkiRJklTr/wfUtxpzSc+FZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.hist(bins=50, figsize=(20, 15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机分配训练数据集和测试数据集\n",
    "def split_train_test(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 4128\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 通过hash散列值来分配训练集和测试集\n",
    "def test_set_check(identifier, test_ratio, hash):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio, hash))\n",
    "    return data.loc[-in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16362 4278\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude']\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16267 4373\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn内置的数据集划分函数\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 原始数据，测试数据集比例，随机数种子\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 4128\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加一个索引标签\n",
    "housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)\n",
    "housing['income_cat'].where(housing['income_cat'] < 5, 5.0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.6736</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2643.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>1.9167</td>\n",
       "      <td>159200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>2.7750</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.1202</td>\n",
       "      <td>155500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2239.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>1.9911</td>\n",
       "      <td>158700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2.6033</td>\n",
       "      <td>162900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>40.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.3578</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1639.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1.7135</td>\n",
       "      <td>159800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2436.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>1.7250</td>\n",
       "      <td>113900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.1806</td>\n",
       "      <td>99700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>2.6000</td>\n",
       "      <td>132600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>41.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.4038</td>\n",
       "      <td>107500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2.4597</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>1.8080</td>\n",
       "      <td>105500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.84</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>1.6424</td>\n",
       "      <td>108900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.6875</td>\n",
       "      <td>132000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20610</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.10</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>1.3631</td>\n",
       "      <td>45500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20611</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1.2857</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20612</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.08</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1.4934</td>\n",
       "      <td>48300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20613</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.09</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1.4958</td>\n",
       "      <td>53400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20614</th>\n",
       "      <td>-121.54</td>\n",
       "      <td>39.08</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>2.4695</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20615</th>\n",
       "      <td>-121.54</td>\n",
       "      <td>39.08</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>2.3598</td>\n",
       "      <td>57500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20616</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.08</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2.0469</td>\n",
       "      <td>55100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20617</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.3021</td>\n",
       "      <td>70800.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20618</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.06</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>2.2500</td>\n",
       "      <td>63400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20619</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.01</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>2.7303</td>\n",
       "      <td>99100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20620</th>\n",
       "      <td>-121.48</td>\n",
       "      <td>39.05</td>\n",
       "      <td>40.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.5625</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20621</th>\n",
       "      <td>-121.47</td>\n",
       "      <td>39.01</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2.3661</td>\n",
       "      <td>77500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20622</th>\n",
       "      <td>-121.44</td>\n",
       "      <td>39.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2.4167</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20623</th>\n",
       "      <td>-121.37</td>\n",
       "      <td>39.03</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>2.8235</td>\n",
       "      <td>65500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20624</th>\n",
       "      <td>-121.41</td>\n",
       "      <td>39.04</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>3.0739</td>\n",
       "      <td>87200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625</th>\n",
       "      <td>-121.52</td>\n",
       "      <td>39.12</td>\n",
       "      <td>37.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.1250</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20626</th>\n",
       "      <td>-121.43</td>\n",
       "      <td>39.18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>2.1667</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20627</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>162500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20628</th>\n",
       "      <td>-121.48</td>\n",
       "      <td>39.10</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>2.5952</td>\n",
       "      <td>92400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20629</th>\n",
       "      <td>-121.39</td>\n",
       "      <td>39.12</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10035.0</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>6912.0</td>\n",
       "      <td>1818.0</td>\n",
       "      <td>2.0943</td>\n",
       "      <td>108300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20630</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.29</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2640.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>3.5673</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20631</th>\n",
       "      <td>-121.40</td>\n",
       "      <td>39.33</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2655.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>3.5179</td>\n",
       "      <td>107200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20632</th>\n",
       "      <td>-121.45</td>\n",
       "      <td>39.26</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>115600.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20633</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.19</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>2.5495</td>\n",
       "      <td>98300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20634</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2332.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>116800.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "5        -122.25     37.85                52.0        919.0           213.0   \n",
       "6        -122.25     37.84                52.0       2535.0           489.0   \n",
       "7        -122.25     37.84                52.0       3104.0           687.0   \n",
       "8        -122.26     37.84                42.0       2555.0           665.0   \n",
       "9        -122.25     37.84                52.0       3549.0           707.0   \n",
       "10       -122.26     37.85                52.0       2202.0           434.0   \n",
       "11       -122.26     37.85                52.0       3503.0           752.0   \n",
       "12       -122.26     37.85                52.0       2491.0           474.0   \n",
       "13       -122.26     37.84                52.0        696.0           191.0   \n",
       "14       -122.26     37.85                52.0       2643.0           626.0   \n",
       "15       -122.26     37.85                50.0       1120.0           283.0   \n",
       "16       -122.27     37.85                52.0       1966.0           347.0   \n",
       "17       -122.27     37.85                52.0       1228.0           293.0   \n",
       "18       -122.26     37.84                50.0       2239.0           455.0   \n",
       "19       -122.27     37.84                52.0       1503.0           298.0   \n",
       "20       -122.27     37.85                40.0        751.0           184.0   \n",
       "21       -122.27     37.85                42.0       1639.0           367.0   \n",
       "22       -122.27     37.84                52.0       2436.0           541.0   \n",
       "23       -122.27     37.84                52.0       1688.0           337.0   \n",
       "24       -122.27     37.84                52.0       2224.0           437.0   \n",
       "25       -122.28     37.85                41.0        535.0           123.0   \n",
       "26       -122.28     37.85                49.0       1130.0           244.0   \n",
       "27       -122.28     37.85                52.0       1898.0           421.0   \n",
       "28       -122.28     37.84                50.0       2082.0           492.0   \n",
       "29       -122.28     37.84                52.0        729.0           160.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20610    -121.56     39.10                28.0       2130.0           484.0   \n",
       "20611    -121.55     39.10                27.0       1783.0           441.0   \n",
       "20612    -121.56     39.08                26.0       1377.0           289.0   \n",
       "20613    -121.55     39.09                31.0       1728.0           365.0   \n",
       "20614    -121.54     39.08                26.0       2276.0           460.0   \n",
       "20615    -121.54     39.08                23.0       1076.0           216.0   \n",
       "20616    -121.53     39.08                15.0       1810.0           441.0   \n",
       "20617    -121.53     39.06                20.0        561.0           109.0   \n",
       "20618    -121.55     39.06                25.0       1332.0           247.0   \n",
       "20619    -121.56     39.01                22.0       1891.0           340.0   \n",
       "20620    -121.48     39.05                40.0        198.0            41.0   \n",
       "20621    -121.47     39.01                37.0       1244.0           247.0   \n",
       "20622    -121.44     39.00                20.0        755.0           147.0   \n",
       "20623    -121.37     39.03                32.0       1158.0           244.0   \n",
       "20624    -121.41     39.04                16.0       1698.0           300.0   \n",
       "20625    -121.52     39.12                37.0        102.0            17.0   \n",
       "20626    -121.43     39.18                36.0       1124.0           184.0   \n",
       "20627    -121.32     39.13                 5.0        358.0            65.0   \n",
       "20628    -121.48     39.10                19.0       2043.0           421.0   \n",
       "20629    -121.39     39.12                28.0      10035.0          1856.0   \n",
       "20630    -121.32     39.29                11.0       2640.0           505.0   \n",
       "20631    -121.40     39.33                15.0       2655.0           493.0   \n",
       "20632    -121.45     39.26                15.0       2319.0           416.0   \n",
       "20633    -121.53     39.19                27.0       2080.0           412.0   \n",
       "20634    -121.56     39.27                28.0       2332.0           395.0   \n",
       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "0           322.0       126.0         8.3252            452600.0   \n",
       "1          2401.0      1138.0         8.3014            358500.0   \n",
       "2           496.0       177.0         7.2574            352100.0   \n",
       "3           558.0       219.0         5.6431            341300.0   \n",
       "4           565.0       259.0         3.8462            342200.0   \n",
       "5           413.0       193.0         4.0368            269700.0   \n",
       "6          1094.0       514.0         3.6591            299200.0   \n",
       "7          1157.0       647.0         3.1200            241400.0   \n",
       "8          1206.0       595.0         2.0804            226700.0   \n",
       "9          1551.0       714.0         3.6912            261100.0   \n",
       "10          910.0       402.0         3.2031            281500.0   \n",
       "11         1504.0       734.0         3.2705            241800.0   \n",
       "12         1098.0       468.0         3.0750            213500.0   \n",
       "13          345.0       174.0         2.6736            191300.0   \n",
       "14         1212.0       620.0         1.9167            159200.0   \n",
       "15          697.0       264.0         2.1250            140000.0   \n",
       "16          793.0       331.0         2.7750            152500.0   \n",
       "17          648.0       303.0         2.1202            155500.0   \n",
       "18          990.0       419.0         1.9911            158700.0   \n",
       "19          690.0       275.0         2.6033            162900.0   \n",
       "20          409.0       166.0         1.3578            147500.0   \n",
       "21          929.0       366.0         1.7135            159800.0   \n",
       "22         1015.0       478.0         1.7250            113900.0   \n",
       "23          853.0       325.0         2.1806             99700.0   \n",
       "24         1006.0       422.0         2.6000            132600.0   \n",
       "25          317.0       119.0         2.4038            107500.0   \n",
       "26          607.0       239.0         2.4597             93800.0   \n",
       "27         1102.0       397.0         1.8080            105500.0   \n",
       "28         1131.0       473.0         1.6424            108900.0   \n",
       "29          395.0       155.0         1.6875            132000.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "20610      1195.0       439.0         1.3631             45500.0   \n",
       "20611      1163.0       409.0         1.2857             47000.0   \n",
       "20612       761.0       267.0         1.4934             48300.0   \n",
       "20613      1167.0       384.0         1.4958             53400.0   \n",
       "20614      1455.0       474.0         2.4695             58000.0   \n",
       "20615       724.0       197.0         2.3598             57500.0   \n",
       "20616      1157.0       375.0         2.0469             55100.0   \n",
       "20617       308.0       114.0         3.3021             70800.0   \n",
       "20618       726.0       226.0         2.2500             63400.0   \n",
       "20619      1023.0       296.0         2.7303             99100.0   \n",
       "20620       151.0        48.0         4.5625            100000.0   \n",
       "20621       484.0       157.0         2.3661             77500.0   \n",
       "20622       457.0       157.0         2.4167             67000.0   \n",
       "20623       598.0       227.0         2.8235             65500.0   \n",
       "20624       731.0       291.0         3.0739             87200.0   \n",
       "20625        29.0        14.0         4.1250             72000.0   \n",
       "20626       504.0       171.0         2.1667             93800.0   \n",
       "20627       169.0        59.0         3.0000            162500.0   \n",
       "20628      1018.0       390.0         2.5952             92400.0   \n",
       "20629      6912.0      1818.0         2.0943            108300.0   \n",
       "20630      1257.0       445.0         3.5673            112000.0   \n",
       "20631      1200.0       432.0         3.5179            107200.0   \n",
       "20632      1047.0       385.0         3.1250            115600.0   \n",
       "20633      1082.0       382.0         2.5495             98300.0   \n",
       "20634      1041.0       344.0         3.7125            116800.0   \n",
       "20635       845.0       330.0         1.5603             78100.0   \n",
       "20636       356.0       114.0         2.5568             77100.0   \n",
       "20637      1007.0       433.0         1.7000             92300.0   \n",
       "20638       741.0       349.0         1.8672             84700.0   \n",
       "20639      1387.0       530.0         2.3886             89400.0   \n",
       "\n",
       "      ocean_proximity  income_cat  \n",
       "0            NEAR BAY         5.0  \n",
       "1            NEAR BAY         5.0  \n",
       "2            NEAR BAY         5.0  \n",
       "3            NEAR BAY         4.0  \n",
       "4            NEAR BAY         3.0  \n",
       "5            NEAR BAY         3.0  \n",
       "6            NEAR BAY         3.0  \n",
       "7            NEAR BAY         3.0  \n",
       "8            NEAR BAY         2.0  \n",
       "9            NEAR BAY         3.0  \n",
       "10           NEAR BAY         3.0  \n",
       "11           NEAR BAY         3.0  \n",
       "12           NEAR BAY         3.0  \n",
       "13           NEAR BAY         2.0  \n",
       "14           NEAR BAY         2.0  \n",
       "15           NEAR BAY         2.0  \n",
       "16           NEAR BAY         2.0  \n",
       "17           NEAR BAY         2.0  \n",
       "18           NEAR BAY         2.0  \n",
       "19           NEAR BAY         2.0  \n",
       "20           NEAR BAY         1.0  \n",
       "21           NEAR BAY         2.0  \n",
       "22           NEAR BAY         2.0  \n",
       "23           NEAR BAY         2.0  \n",
       "24           NEAR BAY         2.0  \n",
       "25           NEAR BAY         2.0  \n",
       "26           NEAR BAY         2.0  \n",
       "27           NEAR BAY         2.0  \n",
       "28           NEAR BAY         2.0  \n",
       "29           NEAR BAY         2.0  \n",
       "...               ...         ...  \n",
       "20610          INLAND         1.0  \n",
       "20611          INLAND         1.0  \n",
       "20612          INLAND         1.0  \n",
       "20613          INLAND         1.0  \n",
       "20614          INLAND         2.0  \n",
       "20615          INLAND         2.0  \n",
       "20616          INLAND         2.0  \n",
       "20617          INLAND         3.0  \n",
       "20618          INLAND         2.0  \n",
       "20619          INLAND         2.0  \n",
       "20620          INLAND         4.0  \n",
       "20621          INLAND         2.0  \n",
       "20622          INLAND         2.0  \n",
       "20623          INLAND         2.0  \n",
       "20624          INLAND         3.0  \n",
       "20625          INLAND         3.0  \n",
       "20626          INLAND         2.0  \n",
       "20627          INLAND         2.0  \n",
       "20628          INLAND         2.0  \n",
       "20629          INLAND         2.0  \n",
       "20630          INLAND         3.0  \n",
       "20631          INLAND         3.0  \n",
       "20632          INLAND         3.0  \n",
       "20633          INLAND         2.0  \n",
       "20634          INLAND         3.0  \n",
       "20635          INLAND         2.0  \n",
       "20636          INLAND         2.0  \n",
       "20637          INLAND         2.0  \n",
       "20638          INLAND         2.0  \n",
       "20639          INLAND         2.0  \n",
       "\n",
       "[20640 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing['income_cat']):\n",
    "    start_train_set = housing.loc[train_index]\n",
    "    start_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350581\n",
       "2.0    0.318847\n",
       "4.0    0.176308\n",
       "5.0    0.114438\n",
       "1.0    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in (start_train_set, start_test_set):\n",
    "    item.drop(['income_cat'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.6736</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2643.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>1.9167</td>\n",
       "      <td>159200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>2.7750</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.1202</td>\n",
       "      <td>155500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2239.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>1.9911</td>\n",
       "      <td>158700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2.6033</td>\n",
       "      <td>162900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>40.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.3578</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1639.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1.7135</td>\n",
       "      <td>159800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2436.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>1.7250</td>\n",
       "      <td>113900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.1806</td>\n",
       "      <td>99700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>2.6000</td>\n",
       "      <td>132600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>41.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.4038</td>\n",
       "      <td>107500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2.4597</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>1.8080</td>\n",
       "      <td>105500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.84</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>1.6424</td>\n",
       "      <td>108900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.6875</td>\n",
       "      <td>132000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20610</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.10</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>1.3631</td>\n",
       "      <td>45500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20611</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1.2857</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20612</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.08</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1.4934</td>\n",
       "      <td>48300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20613</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.09</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1.4958</td>\n",
       "      <td>53400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20614</th>\n",
       "      <td>-121.54</td>\n",
       "      <td>39.08</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>2.4695</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20615</th>\n",
       "      <td>-121.54</td>\n",
       "      <td>39.08</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>2.3598</td>\n",
       "      <td>57500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20616</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.08</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2.0469</td>\n",
       "      <td>55100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20617</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.3021</td>\n",
       "      <td>70800.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20618</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.06</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>2.2500</td>\n",
       "      <td>63400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20619</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.01</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>2.7303</td>\n",
       "      <td>99100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20620</th>\n",
       "      <td>-121.48</td>\n",
       "      <td>39.05</td>\n",
       "      <td>40.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.5625</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20621</th>\n",
       "      <td>-121.47</td>\n",
       "      <td>39.01</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2.3661</td>\n",
       "      <td>77500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20622</th>\n",
       "      <td>-121.44</td>\n",
       "      <td>39.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2.4167</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20623</th>\n",
       "      <td>-121.37</td>\n",
       "      <td>39.03</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>2.8235</td>\n",
       "      <td>65500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20624</th>\n",
       "      <td>-121.41</td>\n",
       "      <td>39.04</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>3.0739</td>\n",
       "      <td>87200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625</th>\n",
       "      <td>-121.52</td>\n",
       "      <td>39.12</td>\n",
       "      <td>37.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.1250</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20626</th>\n",
       "      <td>-121.43</td>\n",
       "      <td>39.18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>2.1667</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20627</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>162500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20628</th>\n",
       "      <td>-121.48</td>\n",
       "      <td>39.10</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>2.5952</td>\n",
       "      <td>92400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20629</th>\n",
       "      <td>-121.39</td>\n",
       "      <td>39.12</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10035.0</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>6912.0</td>\n",
       "      <td>1818.0</td>\n",
       "      <td>2.0943</td>\n",
       "      <td>108300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20630</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.29</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2640.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>3.5673</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20631</th>\n",
       "      <td>-121.40</td>\n",
       "      <td>39.33</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2655.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>3.5179</td>\n",
       "      <td>107200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20632</th>\n",
       "      <td>-121.45</td>\n",
       "      <td>39.26</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>115600.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20633</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.19</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>2.5495</td>\n",
       "      <td>98300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20634</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2332.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>116800.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "5        -122.25     37.85                52.0        919.0           213.0   \n",
       "6        -122.25     37.84                52.0       2535.0           489.0   \n",
       "7        -122.25     37.84                52.0       3104.0           687.0   \n",
       "8        -122.26     37.84                42.0       2555.0           665.0   \n",
       "9        -122.25     37.84                52.0       3549.0           707.0   \n",
       "10       -122.26     37.85                52.0       2202.0           434.0   \n",
       "11       -122.26     37.85                52.0       3503.0           752.0   \n",
       "12       -122.26     37.85                52.0       2491.0           474.0   \n",
       "13       -122.26     37.84                52.0        696.0           191.0   \n",
       "14       -122.26     37.85                52.0       2643.0           626.0   \n",
       "15       -122.26     37.85                50.0       1120.0           283.0   \n",
       "16       -122.27     37.85                52.0       1966.0           347.0   \n",
       "17       -122.27     37.85                52.0       1228.0           293.0   \n",
       "18       -122.26     37.84                50.0       2239.0           455.0   \n",
       "19       -122.27     37.84                52.0       1503.0           298.0   \n",
       "20       -122.27     37.85                40.0        751.0           184.0   \n",
       "21       -122.27     37.85                42.0       1639.0           367.0   \n",
       "22       -122.27     37.84                52.0       2436.0           541.0   \n",
       "23       -122.27     37.84                52.0       1688.0           337.0   \n",
       "24       -122.27     37.84                52.0       2224.0           437.0   \n",
       "25       -122.28     37.85                41.0        535.0           123.0   \n",
       "26       -122.28     37.85                49.0       1130.0           244.0   \n",
       "27       -122.28     37.85                52.0       1898.0           421.0   \n",
       "28       -122.28     37.84                50.0       2082.0           492.0   \n",
       "29       -122.28     37.84                52.0        729.0           160.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20610    -121.56     39.10                28.0       2130.0           484.0   \n",
       "20611    -121.55     39.10                27.0       1783.0           441.0   \n",
       "20612    -121.56     39.08                26.0       1377.0           289.0   \n",
       "20613    -121.55     39.09                31.0       1728.0           365.0   \n",
       "20614    -121.54     39.08                26.0       2276.0           460.0   \n",
       "20615    -121.54     39.08                23.0       1076.0           216.0   \n",
       "20616    -121.53     39.08                15.0       1810.0           441.0   \n",
       "20617    -121.53     39.06                20.0        561.0           109.0   \n",
       "20618    -121.55     39.06                25.0       1332.0           247.0   \n",
       "20619    -121.56     39.01                22.0       1891.0           340.0   \n",
       "20620    -121.48     39.05                40.0        198.0            41.0   \n",
       "20621    -121.47     39.01                37.0       1244.0           247.0   \n",
       "20622    -121.44     39.00                20.0        755.0           147.0   \n",
       "20623    -121.37     39.03                32.0       1158.0           244.0   \n",
       "20624    -121.41     39.04                16.0       1698.0           300.0   \n",
       "20625    -121.52     39.12                37.0        102.0            17.0   \n",
       "20626    -121.43     39.18                36.0       1124.0           184.0   \n",
       "20627    -121.32     39.13                 5.0        358.0            65.0   \n",
       "20628    -121.48     39.10                19.0       2043.0           421.0   \n",
       "20629    -121.39     39.12                28.0      10035.0          1856.0   \n",
       "20630    -121.32     39.29                11.0       2640.0           505.0   \n",
       "20631    -121.40     39.33                15.0       2655.0           493.0   \n",
       "20632    -121.45     39.26                15.0       2319.0           416.0   \n",
       "20633    -121.53     39.19                27.0       2080.0           412.0   \n",
       "20634    -121.56     39.27                28.0       2332.0           395.0   \n",
       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "0           322.0       126.0         8.3252            452600.0   \n",
       "1          2401.0      1138.0         8.3014            358500.0   \n",
       "2           496.0       177.0         7.2574            352100.0   \n",
       "3           558.0       219.0         5.6431            341300.0   \n",
       "4           565.0       259.0         3.8462            342200.0   \n",
       "5           413.0       193.0         4.0368            269700.0   \n",
       "6          1094.0       514.0         3.6591            299200.0   \n",
       "7          1157.0       647.0         3.1200            241400.0   \n",
       "8          1206.0       595.0         2.0804            226700.0   \n",
       "9          1551.0       714.0         3.6912            261100.0   \n",
       "10          910.0       402.0         3.2031            281500.0   \n",
       "11         1504.0       734.0         3.2705            241800.0   \n",
       "12         1098.0       468.0         3.0750            213500.0   \n",
       "13          345.0       174.0         2.6736            191300.0   \n",
       "14         1212.0       620.0         1.9167            159200.0   \n",
       "15          697.0       264.0         2.1250            140000.0   \n",
       "16          793.0       331.0         2.7750            152500.0   \n",
       "17          648.0       303.0         2.1202            155500.0   \n",
       "18          990.0       419.0         1.9911            158700.0   \n",
       "19          690.0       275.0         2.6033            162900.0   \n",
       "20          409.0       166.0         1.3578            147500.0   \n",
       "21          929.0       366.0         1.7135            159800.0   \n",
       "22         1015.0       478.0         1.7250            113900.0   \n",
       "23          853.0       325.0         2.1806             99700.0   \n",
       "24         1006.0       422.0         2.6000            132600.0   \n",
       "25          317.0       119.0         2.4038            107500.0   \n",
       "26          607.0       239.0         2.4597             93800.0   \n",
       "27         1102.0       397.0         1.8080            105500.0   \n",
       "28         1131.0       473.0         1.6424            108900.0   \n",
       "29          395.0       155.0         1.6875            132000.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "20610      1195.0       439.0         1.3631             45500.0   \n",
       "20611      1163.0       409.0         1.2857             47000.0   \n",
       "20612       761.0       267.0         1.4934             48300.0   \n",
       "20613      1167.0       384.0         1.4958             53400.0   \n",
       "20614      1455.0       474.0         2.4695             58000.0   \n",
       "20615       724.0       197.0         2.3598             57500.0   \n",
       "20616      1157.0       375.0         2.0469             55100.0   \n",
       "20617       308.0       114.0         3.3021             70800.0   \n",
       "20618       726.0       226.0         2.2500             63400.0   \n",
       "20619      1023.0       296.0         2.7303             99100.0   \n",
       "20620       151.0        48.0         4.5625            100000.0   \n",
       "20621       484.0       157.0         2.3661             77500.0   \n",
       "20622       457.0       157.0         2.4167             67000.0   \n",
       "20623       598.0       227.0         2.8235             65500.0   \n",
       "20624       731.0       291.0         3.0739             87200.0   \n",
       "20625        29.0        14.0         4.1250             72000.0   \n",
       "20626       504.0       171.0         2.1667             93800.0   \n",
       "20627       169.0        59.0         3.0000            162500.0   \n",
       "20628      1018.0       390.0         2.5952             92400.0   \n",
       "20629      6912.0      1818.0         2.0943            108300.0   \n",
       "20630      1257.0       445.0         3.5673            112000.0   \n",
       "20631      1200.0       432.0         3.5179            107200.0   \n",
       "20632      1047.0       385.0         3.1250            115600.0   \n",
       "20633      1082.0       382.0         2.5495             98300.0   \n",
       "20634      1041.0       344.0         3.7125            116800.0   \n",
       "20635       845.0       330.0         1.5603             78100.0   \n",
       "20636       356.0       114.0         2.5568             77100.0   \n",
       "20637      1007.0       433.0         1.7000             92300.0   \n",
       "20638       741.0       349.0         1.8672             84700.0   \n",
       "20639      1387.0       530.0         2.3886             89400.0   \n",
       "\n",
       "      ocean_proximity  income_cat  \n",
       "0            NEAR BAY         5.0  \n",
       "1            NEAR BAY         5.0  \n",
       "2            NEAR BAY         5.0  \n",
       "3            NEAR BAY         4.0  \n",
       "4            NEAR BAY         3.0  \n",
       "5            NEAR BAY         3.0  \n",
       "6            NEAR BAY         3.0  \n",
       "7            NEAR BAY         3.0  \n",
       "8            NEAR BAY         2.0  \n",
       "9            NEAR BAY         3.0  \n",
       "10           NEAR BAY         3.0  \n",
       "11           NEAR BAY         3.0  \n",
       "12           NEAR BAY         3.0  \n",
       "13           NEAR BAY         2.0  \n",
       "14           NEAR BAY         2.0  \n",
       "15           NEAR BAY         2.0  \n",
       "16           NEAR BAY         2.0  \n",
       "17           NEAR BAY         2.0  \n",
       "18           NEAR BAY         2.0  \n",
       "19           NEAR BAY         2.0  \n",
       "20           NEAR BAY         1.0  \n",
       "21           NEAR BAY         2.0  \n",
       "22           NEAR BAY         2.0  \n",
       "23           NEAR BAY         2.0  \n",
       "24           NEAR BAY         2.0  \n",
       "25           NEAR BAY         2.0  \n",
       "26           NEAR BAY         2.0  \n",
       "27           NEAR BAY         2.0  \n",
       "28           NEAR BAY         2.0  \n",
       "29           NEAR BAY         2.0  \n",
       "...               ...         ...  \n",
       "20610          INLAND         1.0  \n",
       "20611          INLAND         1.0  \n",
       "20612          INLAND         1.0  \n",
       "20613          INLAND         1.0  \n",
       "20614          INLAND         2.0  \n",
       "20615          INLAND         2.0  \n",
       "20616          INLAND         2.0  \n",
       "20617          INLAND         3.0  \n",
       "20618          INLAND         2.0  \n",
       "20619          INLAND         2.0  \n",
       "20620          INLAND         4.0  \n",
       "20621          INLAND         2.0  \n",
       "20622          INLAND         2.0  \n",
       "20623          INLAND         2.0  \n",
       "20624          INLAND         3.0  \n",
       "20625          INLAND         3.0  \n",
       "20626          INLAND         2.0  \n",
       "20627          INLAND         2.0  \n",
       "20628          INLAND         2.0  \n",
       "20629          INLAND         2.0  \n",
       "20630          INLAND         3.0  \n",
       "20631          INLAND         3.0  \n",
       "20632          INLAND         3.0  \n",
       "20633          INLAND         2.0  \n",
       "20634          INLAND         3.0  \n",
       "20635          INLAND         2.0  \n",
       "20636          INLAND         2.0  \n",
       "20637          INLAND         2.0  \n",
       "20638          INLAND         2.0  \n",
       "20639          INLAND         2.0  \n",
       "\n",
       "[20640 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据可视化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可视化\n",
    "housing = start_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185fe0740b8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185fe382518>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x185fe03add8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dLahvglXXDM59zZrlYNs2L5WVAdLTLRQVqaJvp1PN7uuLpiaTXbtU75Zp0ywkJZ0eYTp+Ek7WqRRoWYXJ9x71sGeHF1NKDh22smqlYPXqdn76Uxs2W9eBU2KiwbRp/W+hlp1tZ9my1Ij3N4zTacRQSGK3j5z12mfMsPPaax7y8626fYY2JGw2G2PGjBnq29AGmOl206ZThFE14gIsgCWLoagAjlfDK89JfA5BoBXMZJACnPFgt8OCSVA4anDuKSvLymOPJdPcHCI93YLD0b8goqXF5KmnvLS3q+HnL74weOghJ/HxKlhKjAeLAXWNcOigj/o6AdIkMcFKY6Nk7z4IBkw++0xy5ZViWNR/LV2awnPP1dLS4sMw4OabL4yp19Ewe7aDyZNtOBxCpwc1TRtQ+l+46BqRAZZhwISxML4YZs4SfLlJ4nALpARHCswfI5iZB9ctGNz7crlUivB8HD1q4nZDQYE6T2mpybFjIaZNU1/qtBS45yYo2QO4JbSZnDhuobXVBARHDofIy7Py3nuCnBzopv3NV8rLQ+zaFSIrSzBrlnVAgoAxY5w89lgODQ0BUlJsJCcP329b05SUlfmprg4QEyMYP96Jy3V+v7ZiYkbOiJ2maUPD4nKRoFOEUTV8/1INAiHg1//H4P9+0qSqUmK1wczpBoWFgiVLID5+qO+w76zWM2flSXlufVhRvno7NtFOS72fhjlODh/yEmMLkpzs5IorXDQ1CZqaer5Wfb3JM8/4sNnA41EXnTNnYPpZJSVZSUoa3t+uPp/J6tWNHD7sw2IRhEISm01wxx0pjB079EWsmqZp3THdbtw6RRhVw/sv1iDIz4ff/sagtVUVkg/VZI72donXCykp5zcCNH68hfx8g7KyECAoKjIYN67rEZTCQivj57jY32Jy6Tg7SUkWsqwWWlpUcDlhQs/XamqSmCZkZRnU1JgcP24y5zxaVVzoPv/czeHDPgoKTs+M8HhMXnyxkZ/+NEuPRGmaNqzpFGF0jfgACyAmRr0NlZMnJb//fRCvV3LllRYWL+7/t7nDIbjvPgcVFapJfn6+0W2xOkB7yMqVl0FcDFSchGWLICtRLRUU28tyYaNGGaSkCMrLTYTgqzTkSCSlZNMmDzk5Z47gxcUZ1NdLjh3zMXnyEH6TaZqm9cDicuHSKcKoGrl/Ec9DeTlUVEB6Oowff/6NQA8eNPH5IDtbsGGDeV4BFoDdLigujuwckwrhg03gtKvatIIcSIuwqXpsrODBB50cP26SlCTIyBi5IzRSql5iVuu5P1JSgt8/PJoJapqmdcV0u+nQKcKo0gFWHx08CM89BzYb+HywdCmcb9Cfny8QQnL8uGDu3MGdKXbFbEhJgIZmmFQUeXB1Slyc6DYF+XWwd28Lf/lLNRkZDu64I4/4+K5rzAxDMG6ck6qqAOnpp3+sTFMFVtnZeq1FTdOGt6/vb/KhoQOsPtqyBRISIDUVvF7YtOn8A6yCAoNHH7Xh8Ujy8s4vwJIS9u2HyipIS4MZ03pugmoYMKOXWquR7PXXT5CYaKWiop2dO1u49NLuW0RcfXU8v/1tPTU1AVJTrfh8JjU1QebOjSUrSwdYmqYNX7qTe/TpAKuPkpPVKFZqKrS1qSAmGtLTBenp5z969flGeOdvEBcL7R1QWgYrbjr/+xssoZDkrbdOMGqUkzlzUob6dsjOdnLwoFqgOjm55yApJ8fOQw+l8+mnbRw44CU+3sJNNyUxa1YvxWyapmlDzHC5iNE1WFGlA6w+uvxyqKxUdVhJSXDjjUN9R6dJCR+vg9F5UFcbZM8uPzu3GyyY7yAnp2/BWzAI27dDfT0UFcG4cQN002fp6AixdWsTjY1xwyLAuvXWXPbsaSUhwcb48a5e98/KsrFy5dDft6ZpWl9Itxu/rsGKKh1g9ZHLBd/5DnR0qJYOxjCr6z61rMye3QEcDkF1tcmRIyFycvr2pX7jDdi8RY2Effop3H47XHQRHDsWoL3dpLDQRkyMgbsDrBaIiWDNxki4XFYef3wsTufwGKyOjbUyd+7QBEw+n0koBLGxw+ybTNO0ryWLXiwiqnSA1Q9C9N7CYCgIAdcshjfeBmEYVFYEycsV5Of37afG54Nt22FMgQrYWlrgy82wYZOXtX8JUjzGz7gJgoTxSZTXCQSwaCosnn7+MyoBkpN7Xvx5JDh0yMfq1S0EAnDttXEsXDgyFrfWNG1oGPEu7NFKEf5ZpwhBB1g9crslfv/5N/8cTBfPV+0jyivseNxWZs4Q5Ob2bQTEagV7eJZkTAy0t8OoUfDnNWC1GrS1O9l4OEhhjMn4fAuhEHy4HbKSYUrBwDyvC1FHh6rTS03teaJBV954o42EBAtOp+CDDzzMmhVDXJweydI0bWBIt5vQRp0ijCYdYHWjpsbkd7/z4/XCLbdYmTnzwnmpiouguKj/c0IsFlixAta8DNKEhES47jqoOA5vvi3Jy/HTgAPpMzh2DPLyIM4JlXWnA6xQSLJlS4jycpPcXIN58yxYrRdOoHq+amvhmWfA41E1bHfdpQLXSDmdgrY2EyEMLJa+B2iapml9IQCL/h8uqi6cqGGQnTgh8XhUzdXBgyYzZ3a939GjbvbtczNnThJZWeeus+N2S4JBSEq6sIKLSZPgxz8Ct1t1dXc44Cc/dHD9N/w0Nhn8+L8drNkqcMRBXi7MvwwSO9WA/+1vQTZsCJGQANu3h6ipkdx009C2KgiFJJs2+ampCTFvnp3c3IH79t+xQ7XxyM+HI0fg+HH1caRWrEjklVdaaG+XrFyZgNOpf/NpmjaAXC6sl0YpRfhHnSIEHWB1q6jIYPRoQXOzZMGCrl8mv9/khReqME04etTDD35QdMbjpaUmzz/vJxCA5cutzJ0b/Zc7EJA9LoVzPuLjoSEAv9+iih+XTRJMnuzgvXVQvhOCIbBKOFoOM2bCzPDTN03JF1+EKCgQGIYgJUWydWuIZcus2O1DF2iWlPh5660OXC7Bvn0BfvjD+AErIE9JUSnW+no1+uTqfQLiGbKyrDz6aOqA3Jumado5PG7k33WKMJp0gNWN+HjBww/3PDXOYhEkJdk4edJHauq5hdnbtoWwWiExUbBxYyiqAZbXK1mzJsDhwyZjxhisWmUjNja6wYvHB89vgRibmpn4/Bb48ZXQ2qaGkpPiwbCCMwWunq6W2wFV6G63g9+vZloGAio9NtRpruZmE4dDkJ5uUFFh4vUO3GSFmTNVgFVZCbNmqTosTdO04Uow9L+jv250gHUeLBbBAw/kc/Kkj7y8c9ODxcWCkhLweCQLF0b3O3f79hAHD6rg6tgxk82bQyxaFN0vp8cPgRBkJ6jPy5ug3Q8LL4apM+DIYRMpgjhMkySnjVM1X0IIbrzRypo1QYSQmCbcfLMVyxDPAZ41y87OnQEqK03mzbORnDxw92MYsGDBgJ1e0zQtulwujEuilCJ8WqcIQQdY583lslJc3PXLOG2aldRUA78fCgqi+8c8FDrdEsFigWAw+osJp8TCqEQobQAJFKdBohOMWHj6PyU//r88bNnkI2jCP/4vwbPPJjB6tKqzuugiKxkZBo2NkuRkQVbW0NcQpaVZeOyxeLxeSXz80N+PpmnasOFxwxc6RRhNOsAaYH1tkRCp6dMtbN8eoqLCJDVVMGdO9L+UVgvcMwf216gRmYmZ6n1FRZCnn3YTag1SnGeQlGRQV2fy5psdPPLI6UL2zEyDzMyo39Z5sdnEgNWsaZqmXdD0/51RpQOsHnh8cPgkGAIm5IB9GL1aLpfgu9+143arAur+tkDwtENMDx3pY+wwM+/MbbGxqo7J4xE4HAbNzTBzpoHHY/brHjRN07Qh5nLBxVFKEf5GpwhBB1jd6vDD0+ugtlUVeBdnwD0L1ahOXx06FKSlxaSoyEpKSvT+RbBaBUlJ/T/+cCn88RWYPgn+4frIj0tLs3DXXXH88petZGVBXJzB8eNBpk49tw5tIAUC8Pp7sHs/FBbAimVqaZ/z5fFIvF5Jaqr+d07TtBHC44bNOkUYTTrA6kZFA9S3wZh09XlpvQq2cpJ7Pk5KyYYNHjZv7mDUKCvZ2bG8+64fiwXi4vw88kgsiYnD4w+326Nm+jW39v3Y5GQL990Xz1tvtdPaanLZZU6uuiom+jfZg5JdsHUX5OfC4WOw/u+w9KrzO6fbbfKrX3XQ2mpy++1OJk8e2t5dmqZpg2Z4/Gn62tABVjfsVjAlmKZ6D+CI4NU6fNjP22+3kZNjZe9eH59+GqKw0EliokF5eYiTJ81hE2BNmwSpyZDWz7WMR4+28vDDCdG9qT5wu1U7CItFjVz1JVB0u0O8804LHo/JN76RSFaWCqQ8HklLi0kgAPX10Z84oGmaNizFuWBelFKE6BQh6ADrDFKqxpAS2L0Xmsuhvg4yM+DaiyA1vvdztLQEsVggJsYgOVnS3Byivt6ko0NisUBa2vAIrkDVXY0eNdR30X/TJsPGzfDhOmhphm/fEfmxH33Uxq5dHcTGGqxe3cjjj6tq/MxMC//wD04aG1UrB03TtBHB44YtOkUYTTrACpMS3nwTvvwSqmoBF2QWQm093PYNmDwmsvMUFjqIiTEoK/NjmnDbbQlYrTZqa02mTbORmmoQDEJZBSQlQppuQNlvGWlw8TQ4fgymzoStW2DebLU2Ym9CIYnFIrBaxTktLmbM0IGVpmkjTP+Xr9W6oQOssMZGFVyNHg0NHli/BfJt4G6H1evhJxmQGNf7eVJTrTz0UCoVFQESEw0KC8/tBv/We/BFiZq998gDOsg6H4aA7HTITIdSj+qeHonFi+NpaQnhdpvceON5zBToB9OUGIZuFaFp2jAS54I5OkUYTTrACrPZVMrM71f1Vw2t0L4Lxo0DXxD2lMOCSZGdKzXVSmpq9y/t8ROQ4II2D7S0nhlgtbdL/vrXEIsXW8jJ+Xr/Ed62rYOPP/YwbZqTq6+OQ4i+P9/Zs2HXbiivgIkToKAgsuOSkqzcd19an693vrZta+XVV2u5+OJEli5NH/Tra5qmdandDdt0ijCahk9B0BBLSIAVK6C1Fd7/CBrqoXoXbP4E/rYTKj1Q06A6qJ9SVQf7yiAQ7Nu1vrkEkpPhkrlQMPrMxwxDFW5315eqv07UwJad0Ngc3fP2l5SS119vw2oVrF/fTkNDqMd9S0tNDh0yz0nnJSfD9x+Fn/4E7rxTrXk4nO3d6yYQMNm2rW2ob0XTNO1MRpTeNECPYJ1h2jSw2ST/+zcSIygAgbsOKr6E32XD7kSYVAirlsCJBnjqDbVW34IpsLwP686NzoWH7u/6MadTsHJldL8srW3w9Grw+iA5ER77lhqxG0pCCAoL7Rw86CMtzYrL1fVPZVVVkD//OcCuXYLMTMGcORZWrTrz9bFaVYB8IbjmmlRiYixMm+Ya6lvRNE07LdYFs3SKMJp0gNVJRUWQp57ygFcgvbFIpwXiBd4QHNkHo7JhUwlMGAXxKRAMQawD6lsiv4bHAy+/DOXlcOmlcNVVp9cUHCgdXvD5IT0FGlvUiNv5BlitrbB5M0yYALm5/TvHqlWJVFcHSE+34nSeG2AdOxbkRz9q5YsvLFgsMG6cFdMUrFghL9jlbjIzHdxyyzBbP0jTNK3dDdt1ijCa9GBeJ2+/7SUzUzDtIgMZE4J0CekQNKC+FHYcgbR4ePNdyEuDRdNhTDYsmx/5Nb74Ao4cgcxM+OQTOH687/d55Bi8+Tc4fDSy/TPS4OrLwLDADddCbB/6gZ5shN+/DX94F+o6pRc3fQGvvg5r/9K3e+/MbhcUFNiJi+v623DdOj9btwpsNvB4BNXVkmAwFNXgqqMDduyEisqonVLTNO3CI9ApwijTI1ioWV2mqd4LAYsug42NgB9oBYqAWEiIgTnjVINLiwWWzOv7taRUI1anRq1kH3tZNjbBcy+pQvzN2+DxhyCll+7yQsAVl6i3vnrxI/D6VbPVl9fBIzeq7RMnwJ49MHdu388ZKbvdIBiSpKeZhExBQYHg0ku7Dq4amuDDz9Wswqsug5QIJwa++jrs3A0OB3zvu5CREcUnoGmadqGIdcFMnSKMphEfYNXVBfnDHxppajJ57k/x1JywqgrzJAEFAjKA0SDc4HNAkwfuuEkVovfH/B3uZ24AACAASURBVPlQWqpShJdfDqP62OjT7wczBAkpUFt3blsCvx/WfQGHSiEnE669DFwRtJfoTrsXEl2quN/tPb199Gj44eP9P29n3bUtuOEGB2teCXJgX5DsLIOZMy0sWtT1C//ia6qAX0qoa4SH747s2q1uiIlRr6PPfz7PQtM07QLW7oadOkUYTSM+wNq3z0tNjcmvf5OAP2SDU+mndgEtAmsRWL0wOgOWXAuTs2DS+P5fz+WCBx5QgcDf3oMn/wluWwkTJ567r98PNTWQnX16dlxWJlx9BWzdod5ndSrnMU34f5+GjVth6gSobVBBx7dWqscOHFCjb0VFkBZhh4IbLoW1n6qRoVuv7P/z7s5nn4X48EOThAS4804rWVmnA634eMFf18axY4eJ3S4pKDC6XCxbSqhvgszU0x9H6ubl8Onnqo4s9wLuaq9pmnZeTqUItagZ8QFWfr6dzzZY8PtCMNoOKQIaJNQLCMHUdGiLhenFqgbJyI7OdYVQtT9tbXDocNcB1itrYecuWHgZXL/s9PYrLlNvZztUCu9/DqlJsPugChhK9sF1i2DrF7BpkwrUHA546KGug6xdu2DjRpgzR/WYuqgQJoRbSdg6fbccOgQffQzTpsKCPsyg7KyuTvLeeyFycwXNzfD660EefPDM6nunUzB/fs/thYWAJYvgnU9UgLX86sjvISMDVtzcj5vXNE37Ool1wXSdIoymER1g+XzQ0GwnIB0qsBorVM1VoQBTBVizL4Id9VDdCjWtsKggetdfdSvsPwAXd1Mk7/WqkadIu5MHQyrAOFahasRaOiAkoeIEbNkCY8ao7Gd5ORw8eG6A5ffD2rUQHw+vv65mCLpcKrAqq4DX35GUlYWIcYV4b51qrfDhRkFREWRl9f35n+opZhhqVmMg0PdznHLxLJhYrIKtxAukZYOmadqw0e6GXTpFGE0jNsCqrYVn/whHSyHgt5KSEaTRkGAzwC4gRZCXB00dcPscVeRtNWBvBQRngzUKazYVFPTcefzWlVBVFXl38uw0SHFBXQM4bBBnhRuWwtRxsC4G2tshLg6CQfX+bFYrpKfDiROQmqpGukAFeM++CH//3Mv2LT48MkR8nI3kFCfk2dh6RDDLDhlJfWuQmpkJCxYYbNhg0tgsWLLMQjDY/2ahSYn9O07TNG3E02sRRt2IDbDWf6pGTMaMgb2HYhifE2C7hJPJAktQkJsEBblQmKVGhlxO8AfVyFBPy8jtPglvHwKXHVZOgYzz6CfpcqlRpEj5/HDROLhuIdTUQ2oyPHSreuy22+Cll6ChAWbOhClTzj3eMOD++6GyEnJy1KiSacJf/gbvrAux64sQofYA2EwaZIiOBBMyE3hmnWDjQZhSACsXqdcoEkIIli61kpgm+es7sGGrICsL5s+K/DlrmqZpURDjgqk6RRhNIzbAEgKQKghJTxPYHXbungKvl8DJFnDmwL99G+IS4U8bodGjgo2b5nQ/SuP2wSt7IC0OWrzw6j54cADbGJwtIw2KC+BIKQhDtSs4pagIfvYzFVTG9NAHKy7uzKCutBI2lKiRvlAAEBIcBtgNOtpCtHgkbYkg7fDZPpg7AYr7WCweFyewhL8TB7rp6vlqbYe1X0JGAiydEf0ljTRN04ZEhxv26BRhNI3YAGvR5XCsVDX6XDgfpsyA1R+C2SIxayBkwpYSwS1L4YYpYHVAVjJk9dBfKSTBBOwWcNqgvQ81RTU1IVpaTIqLrV22LDiblJJAQDXrPMVqhTtvgeqTEBcLqSlnHmO19j391uGVvPy+n1anhDQL1FvBDIKUyDgnwYDBF5thTzykOGF5Xd8DrGmTVO2YacL0yae3mya8/a7qUzVzuprFOdQBTWkd7K4Apx0WToTE2L6fQyLZSAd5WMmnn/0+NE3TokmnCKNuxAZY6elw7Y3wl8/AngL2JDBDkgNbJEEbNNfBf/0aDlUJnA41OnT3cvhyh/ojP23Cub2wEp1wxRhYX6rqte6YFtm9tLeb/O53rbS1SVatimPGDEeP+0spWb06xP79klWrDCZNOv1TYbWqtQ6j5a+bAjQ3dECLgKAT8uIhFvACXoOWAFgc0FwLZED7Wcf7A1DbCLFOSOmmRsowYOZF524/Vgp//xLyRsGGv8OkiTCmIHrPrT+KM+HicZCeoBrP9kcI2IMfCTrA0jRteIhxwRSdIoymERdgVddCmwey0+G1TarOqeQQ7C+H3V9CMByrBA04eFgSt1nwzaugth6eeQXqG0ACpRXwD8vOPf9VRXBxngqwHBG+uhaLwOEw6OgI4XSeO3oVCEiOHPHhcAjGjLETCsHu3SY+n+ToUcGkSd2fe89eNQJ02QIYnXfu41KCT4JDnJueC4bg3cMCHEBAgNOAFAu0AS6gCczDkpRZAhmvatMKU08f3+qGZ9+Ehmb1mi2/HOaGR6hO1oO7HdKSISm+63u3WtU/VR0dgOh/8Xs0xTlh5cVnbjvVjT/S9KYVwbdI1P8sapo2fHS4YZ9OEUbTMPiTNXj2HYE/v6X+2I/OhkAI9h1S/a0SYlT9kaUdQmlACCwSAn7JpyWCi6fByVooGq3aCxwu6/46cX0clHA4BA8/HI/bbZKVde6X5KWXmti/X/VquP76eFJSnLS0+HG7Yfz47r+EXi+sWauK1U/WwA+/f+bjzUH4Uz3UBCDbBnekQWKn07V64WSSFessF4E0EyoMlQMF1ZAuAIRUAORIArsfMjulzDbtVotLj85SI1lvfw5Ti2HrfnhngwrILAbc800Y00VasSAfln0D9uyDSy+BvCiOzEWD1wsfrIeS7WrG5aIFcMncyAItK8O82EzTtJFFpwijbkQFWAdKVaoqIxXKquHKhfD5bshMgnnj4ZhT0FQJhxslIUA4JL7WEK4CC/feaHDgCGzYqs51VQ/r+gUCqrWBqw8zCF0uA5fr3AIjv9/k4EEfqakG1dVBdu70kphoIzfXwOOR1NaajBvX9U+F3Q7ZWVBZpdJrZ3u/BRqCMNoBx/3wQQus6DQCVdUOIlZgibUSGGOq/F8TKkXYiOoZlmZiWgW+kCAd8HScPt7nP92c1GpVdW1NrfC3jTAqXT3W6oFXP4Yf3tX167LgYvXWmc8HHg8k9bEtRE+qquDV11RLjGVLIzvv2x/Atl0qhRkMwpvvgdMJsyJMDWuapg0bThdM0inCaBpRAdaEMVCyB0qPQ3EeLJoKB8rhZCO4PVBQCK+8JFh1t4+yFgOHQ1LfaHBTTpCi0XbG5MLkcWrkJS+n62s0NsIzz0JzM1x6KXxjyfnds91uMH68g5deauLkySDz5sUybZqF/ftDxMYKxo7t/l8Ow4D771GtGTIzz328MQiu8OEuQ33emSeoeq+eLAN8BlhMsAO1IWiV4DMRMoi53UL6bAdJSQaNnYqwZk6Akv1QcVKNFs6ZdDrgOvU+zgk1jZG/Ho2N8PtnoKUFpkyGW2+NTpD16Wfq3NUnYN7crl+vznw+2LFH1bsZhmpNkZ4Gm7aoACsYlJSVqd5jaWmQk6NHrDRNG8a8btivU4TRNKICrEnF8MjtqgZrzCg1qnLPdbBxj6o3mj8RUhMhI9FP3QkLps+gwxOiucrgtddg8WLVG6snu3dDa4uqd9q4Qc1WjO3HTLPObr01ifR0C1VVAa6/PoHYWAtPPGHBYlHpxZ44nd0vKD3PBS83QEsQ/BKuPqsIPVVAZQlkpkFDG7S3GciAGc6BhcAIIb0CX00IX2mAcTc6yOh0jtxMeOgWKK2G+FiYOEbVK2WlQlUtJLmgtgku6cOIz7796vUtyIe9e6G+Xi13c76mTlVd9fPzITk5smOEUM/H3SZpapT4g5CRLnj1Vfj4YxOXS32PSQmTJ8OKFQZ2u6CyUq0LOXOmauiqaZo25HSKMOpGVIAFkHPWH2NXDFw758xt8+fDtm0Si0ViJ8T+/XYSElTKbenSns+fkQGBIFRUqJmKjp4nBHZJSsnevUFSUgxycizY7QZLlpwZ/cTG9m9EpLUD6t2QmQAz4yDBAif8kGOHIudZOwegIB5kPMTnw/oGaO6AkJRq2EaE17qRAtka4popkJ9+5imy0tRbZ3ctgw++UCNXV86BK2ZHfv9pqSoYrqpSgWtf0rA9uWgKjBur6tUiGRFzOGDODHjzHZOP3g3gaYOEZJh2kZX3WgR+vyA/X41iqq8nZGRIrrpK8Mc/qhRnZSXcd1907l/TNO28OF0wUacIo2nEBViRuOGGODZs8GEYIZKTbfj9KqzvanHks02cCPfdq9JykydH3tW8s/p6k2ef9VBYaOF73+tmih3g84U4erSdwsJYnM7eL1TfBr/9FLxBSHDCg5dDcQwUnx1YhaUnQVEu7DkE/g4wbJAQI2iul2rmXDA8/VCahEyDP78OqbGwpJef0QQX3HJVr7fbpQkT4M47VNH+RVPOf3TwFLc7RHu7JCnJwG6PLOd43ZXwq/8K0dgArjiBu0WydYuJ02mSkSE4ccJKIAA2myArS/L6W5KsUZLsbMHRo5AdpYXDNU3TzpvXDQd1ijCadIDVhWnTLKxaFcOxYxKHw+Cb31QjU3ldtDnoyvjx53f91FSDlStjyMjo+Q/9jh2tPPNMBXfcMYrLL+89+jtap5qfFqRCWT1UNsLkHpqCJsfDT++Cl96H3YdVvVSyVfDlFxaqyn10SMASxEiwIi0GSXEmm3cbvQZY52vKlK6X+umvkpJ23nyzFSnVZIN77kkmM9PW63F2O6QmC1KSIDERqqslXq/EYhGcOAHJyUEMwwIIDpYKtu2TPL9GzTZcvjyygF3TNG1Q6BRh1OkAqwt2O9x/v6C2VuByQULCwFzH45GcPCkpKBBYLKdTfoYhmD+/99xiUVEs112XzoQJ3Y9ydZYeD9KEEy2qfiiliwWfzzY6UwVZ766D1z6GnCw4Wm6hxeskYAsS63ZjGgYxVgPDr+quLiR1dUFee62F7GwbdrugsTHImjXNfP/76b0fDDz8sMHBgyZtbRKnE667LsS2bYKEBIHNFsTrteBwQMVxyM4UZGcJyiph2bUD/MQ0TdP6wumC8TpFGE06wOqG1aoWPI6E369SgX1NB65dG2LnTpOVKy3Mn9/3fx3S0hzcfHOENwkUpsPdl0BZA4zPhOwelv3pbOMX8NHHcHAvrF8PReNh/EQDf6uVuuo4YkxJfLINX7NBaj9qzgZaWSX86o8gg3DnzTC9U1H90YoQvoD4asmhlBQr5eV+gkGJ1dp7ndullxq8+qqNI0ck777rpaBAgCHZvAt8bRbeWg8yBA6rSe5og8YmuOWbA/RENU3T+svrhsODmyIUQliAEuC4lHKZEGIMsAZIAbYBd0op/UIIB/ACMAtoAFZKKcvC53gCuB+1SMajUsr3w9uvA36JGpd7Rkr57+HtXV5jIJ7fiA+wqqvVTLQJXSx9E4kPP1RBR2ws3HknjB4d+bGZmWqh45SU3vc9ZceOEyQkOCksjHCq21kmZKu3vti2C3KzIT4B/vIOZCRBYT7kpBscOOLA6JCMKxI0eaBkF9xwfb9ubUCYJvyPf4PKarUAdk01PP4oHGmG2hY4VG5h+0FJQpJJVppBXV2QpCQrdXWqVioSBQWCggJBWpqdl9b4OVprRTjAFxAcrRbMHA85ORZ+/IggMQGSuwlsTVNSXR3AbhdkZPSeotQ0TYuaoUkRfh/YD5zKE/1v4BdSyjVCiN+iAqenwu+bpJTFQohbw/utFEJMAm4FJgM5wEdCiHHhc/0auBqoArYIId6UUu7r4RpRN6IDrOZm+O3voKMdLr0MlvdxZOHECVi3TtVmud2wdi388IenHy8rC9DSYlJYaCM+/tx6qiVLrFx1lcRmi3xGoMNhxeEY3J+C4kJYv0F1sE9NAmmByjq1HFBCPDhiBF8egP01kFkE7x+Fa4sG9Ra71dYGrU0SqwVsDlWMvnYjlAq1fmJxhpWimUlUVLcQ6AgSG2uhuTmGX/7Sz7332hg/PvLXevp0VYvW+pKkKF+wY69B1UnJ6DGC+Bi1+HZ8N7MeTVPyyitN7NqlOrUuX57EvHkR5HA1TdOiweGCsYOXIhRC5AJLgX8BHhdCCOBKYFV4l+eBJ1HBz/LwxwB/AX4V3n85sEZK6QNKhRBHgLnh/Y5IKY+Fr7UGWC6E2N/DNaJuRAdYoZB6E0Kl+bojJezYoVoDTJ9+utg9FO5SIMLr5AUCp4/ZtcvLSy+5AUlamoWHH07C6Tw3yOpLcAUwcWJktUHRdPUiFRw0NkKDCfuPgN0BJ2rhv5+AlAR45HdQEAejRsNHB+DyfHAOg++umBgozISt+0wmjzHwpoTYmyhochsY6aonV3ZRDMtnOzCkicVi8MtfqmWIOn89I5WfZ5CSovqPTZ0MJ+oEgQDceEv3wRVAY2OI3bs7yM+34/dLPvywVQdYmqYNHp8bjkYtRZgmhCjp9PnTUsqnz9rnv4CfAKeKiFOBZinlqZbXVcCpaVijgEoAKWVQCNES3n8U8EWnc3Y+pvKs7fN6uUbUDYM/gUMnNRXuuRtqamDGjO73O3gQ1qxRaxVu2waPPaYK33NyYPZs2LpVBVi33Xb6mP37A8THG6SlWaisDFBfHyI3t+tZgTU1ks2bTRYvNvrd32ogWa0wZQI8+xKU14I9RjVcLciXVBwL8r+eCfHeJgtBvwVhGriS4ZIg3Lh8qO9cpX0ffUTw/PMGLQkhaq/w0tph4xqHHbdP8NAcSHGCWlxRfX0efNBOaysUFfX9a5GSBDdeA29+rALwh++G5Vf3Xp/ndApsNkFbm0l7u0la2oj+0dQ0bbAJTv0KjIZ6KWW3HQ6FEMuAWinlViHEok53cDbZy2Pdbe/qmfS0/4AY8b/Fx45Vbz1pbFR/IDMzVQNRt1sFWIYBN96oWjgcPAQnTkJRkWpWOW6cje3bvbS1mSQnG6Smdv8Xtrxc8ve/S2bMiF5fp2g7eBSqa2DhVNhyWC03dN3sED/+sY9tO9rBFIADW3wscTYrzz8HCy8dHp3Ki4vhyScFx/2C1TZJzYkQzjq4MheEhJJqmJmtlkACSEoyOFwOtVthzjT19eyL2VNh6gTVEDU25tzHKyrUqGh+/ultLpeF229P4f33W0lNtfHNb0Y4A0HTNC0aHC4oHrQU4QLgm0KIbwBOVA3WfwFJQghreIQpF6gO718F5AFVQggrkIhaEffU9lM6H9PV9voerhF1Iz7AisSkSbBpk/rDOHkyZGWdfuzIEXj7bbXw8OFD4PfBtdfCjBlO4uMNmptDFBfbiYnp/l+D2bPVmoLJycNv9OpsOclw7TSVHvTUSQ4c8IAZQC1S6CXgMfDFxFJVbfDrp+GaxTB/bm9nHXgWC4yOsfCodGHNBXueeq03VcI7h6EwGVLCwdC6TfDJBvVvjT8Aiy7u/rzdsdvVK3K2I0fgmWfUx9/6lgr+Thk71snYsd10fdU0TRtIPjeUDs4sQinlE8ATAOERrB9JKW8XQqwFbkHN8rsbeCN8yJvhzzeFH/9ESimFEG8Cq4UQ/4kqch8LbEaNVI0Nzxg8jiqEXxU+Zl0314g6HWBFICkJHn1ULW+SmBheii+sulr9MU1JUe8/3Qwem1p3b2JxZNMSDUNEvP7dUJlQrGYSllep0ZfrFoGvTRAKhVDfy+FRWTNIwC9JSw+SkGDllTfUazOuuOfzD5ZYcWYQO2cUFKecDq4AOjrAYlUzEDu83Z9ry044VgHXLoSkxO7368znU6lDKcHbw7k1TdMGVXRThP31U2CNEOL/AbYDz4a3Pwv8KVzE3ogKmJBS7hVCvALsA4LAw1LKEIAQ4hHgfdTcyD9IKff2co2o0wFWhGw2FWidLT9fFUPX1EBlLbQ6Ia4USvarhY4vtMab3YmJgQduh5O1qoA7Iw1aWixMnWpn61YvphlCGCHiUgymT3XT0GiyZXccZTV2Op6De1fA3AgWdd60V40aXT59wJ8SoGZCpp9VS37lAvAFwN0KeRkqIDq7hioUgtfeA087ZKfDwvmRXW/iRFi5UgVYkyZF5zlomqadN7sLxgx+o1Ep5XpgffjjY5yeBdh5Hy+wopvj/wU1E/Hs7e8C73axvctrDAQdYPWg3gMdQcjrYXSioEAt2HvgADhOwv5qiI+Ddh80t319AixQI3Sjc09/npgoWLs2iUcfbeRYaYjGJpMYZxv1J720uJ1UtgrmzIRxRXbe+kTVJTl7aUTqigH/EH9XJsTD/KkqlXdkD8ybBzfccOY+FgtceQkcOqZG9yJlGGpihKZp2rDid0O5XoswmgZ8QFAIYRFCbBdCvB3+fIwQ4kshxGEhxMtCiH609xx49R741Zfwm82w80TP+44dCznFUN0KZdXw1ueQFA9jBmzy5/CRn29l7dp0fv2rJC5bYMNihGhulIzKtmC1CCrL2zl8KEAg2Pu5AC4qhFm9rOVompIPPjA5fnzAJn9QWalGqbKzYd++rve56jJ46G41mqdpmnZBO9VoNBpvGjA4GddTnVpPOdVFdSzQhOqiOuy0B8AbVDPB2iJoon+wHLJS4OYrYWIBrLwa4rqYQfZ1ZLcLxo2z4/HYABvBILQ0WogRfto6rLzxToiqQyE87uhczzShtFTS1BR5gOX1Qm1t5NcoKoZGDxw8Aldc0Y+b1DRNu9AYUXrTgAFOEfaxU+uwMjoJbp0C7gDMjWAkanw+7DoMLR41cpU1DNoTDKbYWAujR7vIz7exdauDPXsEeXkQHyuJz/IRZ1g4etQSlbYNVqvgO9+xUFsLDQ2RtYJ47jkoK4P77++9LQdAixdEDiQnwMX9mEWoaZp2QbG7IF8v9hxNA13t0pdOrWcQQnwb+DbA6L4s8BdF0yNfR5lZEyE5HtraoSgX7CNoKbnjJ+D9ddDSYcXd5OfECQcOh8TjgbS0EDJkITnZSnYf10DsSUkJvPqqmtF5110wvpe0os2m9o10Qe7cDJg6Blw2qKiE0Xm9H6NpmnbB8ruhUtdgRdOABVj96NR65kbVVv9pgNmzZw9csU0UFeb2vs9w5vWaHDgQIBCQFBfbSE7uPRoJBuH5NerYLTvaqa70InxeioqcZGXZ+cEPHGRm2sjOFmRGseB/zx7V7NXvVxMMeguw7roL2ttVm41I1NdB9SEwQ7BvJ9x4Pcyddf733Rc+n+SjjwJUVZnMn29l2jQ9J0XTtAEyNIs9f60N5G/svnZq1YaQ3y/54x/dlJcHsVjU0i0PPphAenrPP3HBILR3QHWVj9qaABILwYCkvNzPNdc4WbzYjtUaeQNVKeFoBXg6YEwuJHSzft+8ebB6tRqR6mmZo1NstsiDK4DPN0GME9JSVf3WR+vODLCOlavnPrbwzL5o0fTJJwE2bAiSkiJ4+WU/6ekGOTm6wEHTtAFgd0GeThFG04AFWP3o1KoNobKyIJWVIQoLVW7z+PEgJSU+lizpee0epxMumQNPfgiGsGAE2nHGCWw2yZ132iIKrkx5epma9Zvh/Q2qnUF8HDy86nSQFTKhsh5cTtVP6okn1H7OfjQ/r60HmxWSu1mRxmFTARRAIHhmyre8Cn7/opoAcc8/wOReRs/6q7ZWkpio2mE0NUna2i6IgVxN0y5EATdU6xRhNA1FzmHQuqgOFlOq0dWBGskYPH3/Ax4IwMEDMHY0dDR10GAGAcn998cxZUrPTa9KPfDWSTjeDsVxcMMo2LANcrNUQFNaBWXHYep4aGmDb/0LlOxWAdf3VsCSBdDmhrxRENeHNRw3boZ3PgaLAffeCoX55+5z5SIorYCKKrBa4K5OC3lLGX6lIni5Wlvh7Xeg5qRqLLp4sVo8OxIXX2zhhRdCtLWFSE83yMvTo1eapg0g/SsmqgYlwIqkU+uF6lATrD4MqU64d6Iqio6UaaoO8LGx3aevQiHJ3r0BgkHJlCl27PaBieIKCqyMGmWltDSAYQgcDpg1q5euoEBbG9TVwYJLYsgfbVBXF+Tx71uYOLHnYaUtdfD9zVB6CLz/P3vvHWVHeab7/r6q2jn17pyTUiuigAIgkhHBIEww2BjbOGOwx2YchrFnzh2vc+7cO3f5rLkzPr6eY4+xjQ2DjY0NmJyFhFDOWS2p1TmHnVNVffePr2VJoKxGYLt+a9Wim127dlXt7t2P3vf9nqcf9Dz8tAqu9oA1BqURJWQCPnWf/vnHsHKdqjjFkvDYs/DmSqivguKo8qPynaEtxuYdUBKFRAr2tJ5YYJWWwNfvg5FRZTwaCh19rLEObr5Jub1PP8WKRCnh0Uehp0cihMmzz2rEUjreMExrVtupmDrV4IEHNOJxSXW1hs/3Z6/gHRwcPqi4glDjtAgnEmdq9jxZ2QteHbqTcDgOs87QhsC24TePw85dqqLx+c8qV/h3smJFjpdeyiIEHDhg8bGPnUWp5ixwuwVf+EKQ3bsL5POSqVNdFBeffuKxsxO2bIIVb0BTs4dP3OUhGlUD5f53nGo2C0+9DC8dghVe6NsFhUNgFQAJw3tgfzFcWQ3zG+GGy6G5DnYdgCdegcEhGOiX6F6BT0JQQMMi6OiGvkFoOsPFpovnwVMvqSrZrJaT7+fzQc0JRNtQEl5sB0vCpCZoeMd7PjACw0Nw+CDs3i2JxRIMDZnk87CzK8yVVxms3w5/9yWIhN59/FQKXnoNggG45iqNsrIzuy4HBweHc6aQhF6nRTiROALrPJlTDE8fhrAHqgOn3f1PxGKwazc01MPQMKxbf2KB1dFhUVys4fVCe/sZ2qGfI16vxvz5p69aHcE0lVXCh65S1zA6CqND8IMfqIrcV7+qKj9SSrZuNfnDcxZbOgz2tWiMbtUotKu5KmKABSCJ5QWvC1i+DK5apNzUv/l9ONyG2tkGKydptQSNpYKOLvB4VEXqTFk0HyY3qcH30EmG6E+Foam2ITa43qFBN+yCJ16Dda/CzEbYtNlmxNaYXucmN2yRz5vkCgZul2pRnojtu+DtdWqAf+pkaDxBhc3BqPEAMgAAIABJREFUwcFhQvlghD3/ReEIrPNkUSVMjaoqlvcs7mYgoMRHdzdkc1BzEjPTyy9388gjaZJJuP12H6YJ+/dDRcWZGWy+l0ipKnF+PzQGlejYtg10t/LGOtL+/NnPTJ5+2gS34EDQZqRfYvcLLFtAChA2aBLSElKQjGj8z6c1lkyDoQFYuYVxAYb6AMgrkXXTjYK5zTC1WbXxYnG1SyR8+nMvPokgGxmBaPTU83RFfvj61UrvlQYhW4BUHor9sLtN5SlKG5JZKK8WtHcY7OuymFxi8dlP2DRPg8YaVaE6EXU1EAxC0K9alQ4ODg7vOa4gVDstwonEEVgTQNGZF33+hNsNX/w8bN4CRUWwYP6J95s82cV3vhPGtiWBgMaqVfDUU1BeDg8++P4O1rtccPPN8Mc/KrG1eAm88Dqseg0aatU5rlwJjz8u8fs1MklBoUpgxkFDAgLcEnISTAk+AUM2DJr04eaffgouATKAClUyUQIrA1QIGifD0vHg5K074Ynn1Ncf/wjMnn721xOLwRNPw/IboPo0pqjR8fbnSAr+cy0kcrCgFi6dA4e6YeEV0FIGcy/SONBrEO+3mNTg5fLLfbhPk75ZWwPf+YZqHZ/pQLyDg4PDeVFIQr/TIpxInI/vC4hpQjqr7AeEgNJSuO7a0z9PDTcrJRUMKnFWVHTu4mrdui7WrOni3nsX4Pef+VR+NivZtUsSiQgmT1YvvmiRskywbfj1kxBPgy8ItfWqSrd7N0SjEiklgQAMuQV2wCKj6yqh0o2qTh1ZkdcINEgKlsTrF1QHwReAvAeQ4/XrKBR/GLqOCVlevQGi44Pxqzecm8CKRODW5VB2FuHN+wchnoX6KGzqhBta4B8/r2wnjoijizE421+1c7GecHBwcDhnnBbhhOMIrAvE8Bj87GllNzBnKty5THk4nS3z5kFDw/Gr2s4WTRPounbWAu2FF2zefhsMw+b++3Xq69UBQiElHju7YekSaGqAijI1Q1RdDRUVanVifz8E3Bah6TqdcYGnB3LDQBDISGgGSiT4DEQIRsMwtB9m1EKhATr6QYbAH4Q7lkOnhLQFfh2mNMFrb43fo1nvPnfblggB4jQXXX6WA+VVYfW51D4KtRHVJj6X9/Xhhwts2iR58EHDsWNwcHC48LiCUOm0CCcSR2BdIDbuVrYA9ZWwbR8snQs15ed2rOLi8zuXhQtrWLjwDBKs30E6rapnpinI549/zDCU4ejq9aqydvVS9f+vvx4SCR2PR+D3W1xzo+Atv05rn+DFDMQ7BbnO8X86NUlw6bgTGtWGoF1CpQnXzIeUCasNcDeDWwdvUAkr97gWWXaFakuCclc/ltFRyU9+kqO8XOOzn3WhaRPXV20ohq8uhdEMNBefm7jKZCSPPy7p6REsXmzxqU85AsvBweECU0jCgNMinEgcgXWBCPkhX1AeTpoGvnOY23q/uekmjWDQprRU0HwCD6cbr4X5c5QIKxkXgX6/ygG85x6NI/XnljT8UofRadAeALNZ4/CwpFAKgT6IaIKFLZDXYDJQV64GyXe1Q9SG6EyoLoJbSuGIUbymwbTJJz7vWEwyOgr5vI1pctoZqLOlOqK2c8XnE3zpSxrbtkmuu84JA3NwcHgfcLIIJxxHYF0gFs2CRBo6+2H5FVB8Hn+Q3y+KigS33HLy30AhoKry9Mep8sN1LmjtgYIEtx/m2IKuDkhOUqsBD43A7DR4vNDep9zyP3EF3H09GMbRdl/egs6E+myoC73bNgGgsVHj3nvd4/NrH0yzzuXLDeZeDG5n9srBweH9wAhCudMinEgcgXWBMAy4/tL3+yzeGwYT8MxaWLUCBvfDvDnwdw9A+BR2Cd3d0GDAwgro7IN//Aas3Qy7e2FtBhYUwUAKblgMRWFJoQDTGm0ee8xk9WoTTdO4ZKnByBQXw6Yabq8Pw2dnQHxMvUZZ6dGFAJMmaSST8PTzyvvqysvUjNjJ6OmR/OY3NnV1cPvtGrr+3gkzy4KfPwGdvaq6dv/dUO7YMzg4OFxIzCQMOS3CicQRWA4npFBQovB0g/DxDPy/L8GrL0oO7wCvgN4uQVMDfP6ekz9v7gzYvAs6euHSeaqV+KGlcJUN0edg90EoCkFdueR3j5ts22aydm2W9vYkuVwBdBcPPeIhXBli+jIfLXMhNxV+/CQMtKqK1pIFsPz6o9fwxipYswFsCyrLYcYpXNy3brUZHFSBy1ddxTm5qe9rU1mKC2ZC6SmMUONJJa4aalT+YlefI7AcHBwuME6LcMJxBNYEsXYQHuuA2gDcNwnCx7gfpAugC/Cc5G5LCbsPQ/cglETgoknvr//R22/D888rH6vPfe7UKxb74tDTLTn8uiSTkiTcElvTGBs+9aB2XRV88/OQyUHZMUP7tg2fuAmGRlWoc+t+yeHDktWrTQ4fBssyQdPBE8QGxrozbH/NjTB0SpPQPwRLZ6hjrdkElyw8atZpmhAbU0aifj/kcrB3r/Lzamk5fkB93jyNffts6urEORm6jozBI38EpBJaX//0yfcNB6G+Gtq7wedVYdcODg4OFxQjCKVOi3AicQTWeZIvwEt74Htb1YyV2w8zw3BTNeQteHI/7BhU+15dDx9qeHdV6JUN8Npm8Lkhm4ddbfDJa0/dwnqvsG0lrqqqVM5gayvMP4kJKkBxAKws5LIgvBKyEtw28+edWmCl08rJ/NiomvUbVQtvZgvcdYcSPBUVglTKJp0WSHkkKujIsQXogkwsQ09HEBGCep8SrEc4cq/7+2HTerDScN1t0FgPjzyqsiClhGXXHO9JVlUl+MY3zv0N0DT12gVLCbhToevwuY9Cd7/y8io6Ayd6BwcHhwnFTMKI0yKcSByBdZ48twH+7XXYvlOF/woX/K80FH8CxkZg6wA0htVjL7dBXRimHlOxSaZh5TZoqIADHbDnALy+BuJDcNVCmD75wgotTVOZiAcOqK9PZwlRGoS/vwt2vSI5uEMQFjZXLIWlS0/+nGefhdWrlUHpJz959PrWb4KAH3bshuVJSSxm4fUK7rvPIJWyeeUVk5ERAXYGcgXAAJcXXdewCvDppTA5Aq+vglQa6mtgLKZWNG7fA5v2gd8NReOGpPv2QVOjEnv79p2Z6euZUhSGL3xUtftmTTn9/m43NNVN3Os7ODg4nBVOi3DCcQTWeTIcV6verDy4A2AKeHsT/PZKkCmo9qhKhiGUf1N/6niBZY5n7I3GYNNO2LkZRodh12tw+21w5y3wkWXndm5Sqlmqs7Ul+OQnlcAqKoK6M/ijP7tBsOllnd27wTR1Zsw4eYuzUFAtyPp62LNHZf8dmW/60JXwzAtwxWXwk58kefrpLB4PfOc7Qb79bTfLlkX5+7/vZGQkC9b4J4HhprzGxcIW0DKw9HpVAfvRQ+rYP3sEbrsZ3tgAc+Yqw9fBhHpPFlwM69erw9y8/Ozu0ZnQWKM2BwcHhw88ehCKnRbhROIIrPNk+SJYuw92HlKJL4YJroBqDdX7IDYERV5lM5C3oNR3/PPDAagthze2w9YhiPuAckjvha0bobzy3AXW1q1qxugTnzi75/l8MHv22T1H02DWCRzU34nLBXPnwpYt0NSk5qGOMKMFpk2Bx35v84MfZtANtXrwgW+n+el/lvD5zxuMjJTyL//STyIhsTWdaLWXmz/sZd5F0NYNL71pM3e6wLIE9TVwuBN6+tXxJzVBZASGR9T3t9wMc+coMXgiIdndDaNjMGP6uRmITgR79qmcxZuuPfWqTAcHB4fzwkrCqNMinEgcgXWeVBbDf34NMmaGV7cZFPwuLl8Kngx8ega8aMGBMUDC0lpoecfAtKbBLVfA8/vBzgFJVJm2EXJ58J5kfmdfBzy/FpqqYPklJ64YTZ2qBtU/CEgp/xRTc8cdcO21anj+nee9/yBs2QbZjMnwiHJ47x2Ae/82z9fudfM3fxMhFrP57RNpRChEZWOYhvHIHqTNL/8ry/CVgosv8rFxG9RVw2WL4HCvGiLXNFg8T+2uaUrknYh4Dv7jYUjH4L4vwrSp78ltAWA0CxlT0leAiEvQkYa1rRB2g7UDdm5X2YqzZrx35+Dg4PBXjtMinHAcgTUBDGsHuf9ba7l+wMX+vtnUu6ezN2Wx1czxkdkutJwLXUD4ZO7tAi6aAlVueORX4229EphUBR+5BnYMwVu90FIEiyohloWvPawqZoF90FQDF53AWT0QUNuFIp+XrFtnMToqmTVLp7lZI5GQPPxwlrExyT33eGho0NG04ytXR9i4GdZvVsJSdwnAAMMLhouuTovnX4MbrtH43veKueHDEXYe1NjTpREJKy+pvCm4ZIHO3Lka8+bBDctUaLKmwZfvVpWsojDggsc2wVAKZlfBFZNAH69QSQnP98GaEdjTAqUDZxf+fCaMxuDp16GyFGbPhX9eJ3lmDOK6hdQtvNsMmst1LquF+TPhKwtVZc/BwcHhPUMPQtRpEU4kjsCaALazncPkyZeY+MQ2WtuncsiVYIcvTpfQuNtbReAU/zSI+MDQYUoL3HU37DgAYT985lLIl8GDb0LBBo8OC0ph1xCkNAhZMJiB1jG4aIKvqbcXdu5UVbCGhjN7zu9/r/yqAgHBmjUW997rJpGw6e6WBAKwZo1JQ8OJ70NHJ/z+STWTNjYKwZDGwIABhhukTTqt0X0oR2uri/17TV56qUAwKLh6qY9NuwRCgxsuF1yzxIOUsGoXbG+DmlK4fp6KJmquh7wJP1wFyTyEvfDCXjiQhVmNMNsPvRlYNQSNAXC1wOvF8C/t8HkdKnwQNsA4z3bhtr2wsxX2HIJ8EazqgEG3pJAHV5OFLnTaDoEnDTe2wNxJ5/d6Dg4ODqfFSkLMaRFOJI7AmgB68eOhH58uEMXFXOYqsN6fYJLbQw85kpinFFh+D3xqMfx6PVQ1Q9MkuGOuip35/gYo8kAqD8kMPL8bhjIwowmm6Ep/GP6JvZ5CAX72M+UT9dZb8O1vn9oLSz1HsmOHRVOThhCC3l6bvXstFi3SCYfVSr1Zs05+D6StLCKEgBuXadx9e5j77kvQM1xAagZ+t01FqeB3T+bJVep0JV1cHLK4YSlMngr9YzC9Vj1/exs8ux4qorB+vzru3LkwkJcIy6YtLZgeFbiEYNiAFR0QMsFnQksIejWolrB5VIVJJy34+nZYVAplbvhsPRSdpHXb2gu7umBalTqfEzG1CYq2wUAIfH5o8QoCPujcp8FBD4WcTsQNdQGYP+6JZUtImxA8jeWDg4ODwznhtAgnHEdgTQAVzKCdIBqSWr2Gy4rcjOKmmxyN+Cjh9Mv4JpXDgzfAK4chYUFZBfSklGC4qgpWtIFmQyAIfUnoj8NV0yFrwoyTtLBs+9yGs6XkT6HIuZw6zukwDLXqcGwMIhFJOi0pLhYEQhpf+7oPJIRC77aFt214+WUl5BKjMGkKfOQm2NfhpWaBwH3QorHGxMzAkoUuXtksWTRVULlIx+c32N0r+O1q8Lhg5R746g3QO6pEa9CrDF63DcLK/hiH/N20D2t0dDVQPOxmbkBjgxSM+kAfhUzAZlXMpsilkdQEcVPg1aEnr+5JnQ96s7BiCG6tevc9GE7AL1eqc9lwCL5+A1ScIHOyuhy+9UX4XQya3XBFGfTHBfocwR+3Ql4Hfwhm1IOpwZox+PftsPstiMThO3fC8gVn/746ODg4nBQ9CBGnRTiROAJrAricakrxYSOZQRQXGrdQThYbHxqCM8uxa0/Cyn5VNUmbcGUNIFVL6uBBGGyHSBRmToLmMMytVOJq6gkE1p5D8PjLUFECn1kOft+79zkZbjd89rOwcSPMnAmRMwimFkLw6U+7eeyxAl1dksWLDWbP0fm3pyGZEAQzMNgFSxfCzTcfNd/cuhXeeEN5b9XWQlsb7NgBL+8UNDdo9LZadA9CxCfoj6lZq3BAUFKl0R+Dfd1QFICSELQPKnE1qQpW7FRVrWQWmuYUOBTqoT1l0HUwiihJMTRk8MqQIFEpIAeM2VAvsaREeArstQzme3RKDNiXhQXFyt7Uo0HKOvE9yOSV31lxELqG1fcnw6fBPeNzaF/5kIoc0rx5jFrB4KiLggn+avhv2+CtDPS9CslWVWn7+3+F6395egNTBwcHhzPGSkLCaRFOJI7AmgC86MzjeJWjIfCfoN4qJQyMKHPN0qLjH/PpquKSNdUKsoYwlPrhj21KYBUsm6FOSS5o86UFOre2nLw89fpGCHihvQfaepQoOxsaG9V2NlRVaXzrWx5sW6JpAstS82Xr1sHet5QH1RO/VSaj3/0ulJbC4cPKfuCI2Wg0qkSW3wtTWly098C+3TaWC15806Zxis7WAxrtr8PMJmgIwlgKeobgcB9k4hANwuQyFYczqRoamy26cvD27iCZvgDBkiSe5jzDq3xQCowCEQl5tXAgJwVxYRE2dOZEYFYI1iVAE2oY/tKT5ApWR2HpNNhwEBZPhvozjNgxdEgHh9lAKw1zdRoHm1jkL+V7+2z25GKM2jpiOERIF1T7wEirhQCOwHJwcJgwnBbhhOMIrAvMc2/Bmu0ggZuWwmXHTKfXhuCLMyGWg+nFqnK1vBl+sg0sn401BMILGcMk1DIGnDyBOKzDE09CtBhCd7znl3UcmiZIpyWbNtlovRJrSKOvT+DyCoRQ5p4/+hHcd58SWanUUbPRZFJZS1wzDx56UuALu0n5bYaTEnKSQy9akM6huyHWDFra4KJLDdZ1wsxqaKwA04aOAXAZcPVsKDXczMz7qRhyMeIzifeGKaowkHkB3UAJ4BMwbEPAxqXbpEZ0NmRhYxcsDClh3BCEW5ug0nuy64ab5qntbNlDF348uHQ3a+1B4v2l7LNHuMv7/yHcBruWf4MDvwlgFOCWD6uYIQcHB4cJQwtCyGkRTiSOwLqAJNOwZgfUVYBlw4tr4JLZR+ekpIREEjZ2qhmehbVQGYGuXVDoBSTIrMCtW2zsLbB9PUyqhLsuU1WQY+naD7Mmw6Zt8NCv4B++oSwLjmVgFLxuZXZ63teWgUdfhXQOPn6l5KnfmvT0SMJhQWHEQkfHnQefW7BkCdi25Fe/srj9do3mZo22NnWc2lq47DJVfYoE4a0946k4QqhJ74yAnI2Vg8P7YWwwT1rAh640/lQFc2tQWwYjCXh8BTxwu8Zydy1bIjlcGY19upsmoaE1Qt8GiRyUSmTVSHzlNjKpoQ+5mFYBG4dhWwZCNgz6oXL6+d+rExElwGEGWNvhZ0VrJYN50D0eYt4Qda5RvrzUZtIlKjHACYN2cHCYcOwkpJwW4UTiCKwLiMsAt0uJkYIJIf/xQ+ivH4CX90OJX1Vrf70NFlZDqhcICKXAbDD7bXq3hWgS8Ic3gQTcdf3xpp3RKPzHryCVUEPzdfXwpU/yp/Zd7zD86I8Q9MG37lDndj609UFbv2qhvfK2pLtb0tioLq4sIpjdaDNtmkZjo6CkBHbvNnnjjTyJhOAb3/AxNCJAQmPD0dbX73dC7ki+swTyKJElbJBg5y3ytouNrxVYfLFxnNN5AtgZAt8QdA6CDOi01PtpqYC+PHQX4P4F8Mh8m52mTXIQTI8gl/HAqEZVAbSccuTXNEhb4H7HsH8yC79eDZkC3HUJlJ/BrNrJuIhGAnh5w2vQ6fIz5oGIEaLVfTeGobN7OMSXKmDeGbYdHRwcHM6a9ymx4i8VR2BdQDxu+PSH4ZlVasbotquPPpYpwJuHoCF61Gcp6IF13SqgOO0VoEuIAwk/VxfrvLkF4iOwdRtcOlv5PEkJT+6HJ20Y1AEBh1vh+/9m8/OHLMoi8OC3NabP0fG4IOBRs0XHEovBypUqPmbu3HdfR6EAr7wC1dVHH68rg9IwZHJQGpC0HnPMoiI1axWJQMm4QMjnlfWDx6MqTb9+Ra0o/OLtqkIzEoMDPaifUBP1i29ZaimlrqtljsIim9WIjxm8uUpy/bXiT7mLY0AHUGLAUAKeGVD3NW7BrEoYycLv22E4IYlMyRBpzJEVMNzpp6QzwPQAHBiBa6vBGgG3F255h5v73h5o7QePAWsPwEfOY2WfC4MwlVAeo8JOUL4/yi0+wf+4uIrOMXj2kFrg4ODg4PCeoAch6LQIJxJHYF1gmmvhgRNkAybH7RCONbHUNfAZcMdy+K82sAoabgOua9EIB+D/uAN+9Xu1QrBsPEA6XYAnd8AbcTAXA8+B9EpG+iz6esBbLPjH/9vmjWc1/u5jAkM/OmB+hBUrVCCzrqtB96J3DOMPDsKLLyoD0iMCqygI3/yoKjQNDmi8/bpFoSBxuQR1dTZbtwpsW5DLQSIB0ajBPfdotLQIWrsEybS63sM9ysbgkeegNAjdLlTlCpR6NCQYFiQLAJgZE1+ljmmrcOdQSLnXVwPXoAxF3S6QGbWAAOBADqzxwfi4bSO9WXRDgIBoVYrCgJexpI5fh/lVcOUMlSdZ/Y4KVVUR+NzKHLV5AiKJStC42u1mWaXOvKCgNqLE78s74FAfPDcGX7vm/F/HwcHB4V3YScg4LcKJxBFYHxCKfOA1VCXLN94iy5lKdNywGKqmgNeCtiFY6IJLJ0NZGL57vxpPOiKSAm5ItasWJAG1BWohc1BgFQtst6TN1PinR9QfcEZzuOwcl1ziYcYMleVTU6OOV16uZqHeSVUVfPnL7467OXIOVVWCG2/UefFFS41O2ZJvfEPDsgRtbaoy1tICO3dKhoclSy4VNNcKLBtmToZsDgZH4c4FyuF9NC0AqSb3U5bysMACC4TbS86tY+YFq1dDLm1T5LeZMUfgK9dJZ5T4+1QU3hyAhcUSM5TiEcsko/lJuXOkdYmJhisPhpC4IxaukM5cF7zdB1NC0OCCti5le3HE8qKmGL55oxJYJacxYj0TvGjcQYjdQ/DiQbhqMrRUQWMJHByE5gmO7HFwcHA4DqdFOKE4AusDggbMLoHn9qtZHk1Tq+HunAOuEGxPQN6lYlPubVKD3HDikOdQHnge9e56IJwV+Go1REji1QT+CkF7N/z2eYvkkMksr8mhQ3m+8x0XoZDGxRdDc7OqBLlP4JEqhPLHOhVLl+rMmaMxMiKJRgXhMBw4IJk2TcXv/PCHJvk87NkjqagQ3Pexo2U0KaG8GEbj8KlL4emVgrGUwOUGLSwY3FIAoSE0yaRpNsXTdUZHJMURSX+XzYgLenotzCaNefME/3sV3LMYvjAJ1jHMZkYprxCU+S2GbB+m24emWRRwIfIaCT/sGZMYKUHehF/sgMf3QM8B0HT4wYOqEgkQmWAXfYA/7oCYZfHsTp2WKrh2us2lvi78ddU4v7IODg7vCVoQfE6LcCJxPq0/ILywAd7eAW5TVUgWz4AZFVA1Prh9fyPETGj2K3E1OAKbdyshMne6Ej1H+PJNsOItGEuCuwiyGsTjGqVBSXW9wLZh+17omyywIhq7VliMDWtU1eT48pe8gOCZZ2FgAD5zj6pknQvhsCAcVie2dq3Nk09KhIApU5QwTKWUmHqn27wQ8Omb4LlVaiXhY/8M5RWwq13jpTd9uG9ysX9vHl9Y58f/l5vHn5Q89jub3YdBIigukgwk4ea5MHs6JLLw3E6orTB5kxgVePGaMGxkyAsNTVjotoVm2TRme+gXVSRlJWlbUOyBJh+83AnePOSzsL/jqMB6L5g8Jc+juTE+540CLmjdReDX/wkf/QzZaRfTNqDsampKJAHfmZnYOjg4OJwSmYSc0yKcSByB9QFhdztUjw+A57JwzZTjH68fr5Rs2Q2rt8D6HVBboea+/T6Y1nR030tmwyP/HfYegpdegrc2gCUgERd8aLYaTv/pS5DNCwrCRbovSE87bNwueObZHL/8uZddu9WxDxw8d4F1LK2tkmhUtRUPHZLcf7/OmjU2paUac+a8uy4dDcOnblJfH+yHh15XM1S7ewTzGlwsutJFcQT+uA5W7LPxV8FV9TZXXqUhTcmaEZ3KakE6q+bakqbNwzLNGnS8tprNGoz5EcU2hQEPuUE3fj2LHdXxGSY+DYQG88PgQ2VEyk5YPAUunaPm5Q4PqEieygkePr+52WCyDDJdjFf1Kmvhkg/RF6zlwZ+O8dpaH8PdGpHaHP90v+Srl0xAf9LBwcHBMRqdUByB9R6Ry8OhTmV/0Fx3tEpjSfjDAOxJw/IS9QccYOE0eGWT+vrak6xG6+6H370E4SAc6FAeVtGw8tc6FiHgiovV1lgGGzaAG6gMQEGDWFZFvpS2Q99OiSxIkBJparzyhs7OXRZXXqEzNAQzZ0zM/Zg3T7Bnj00sJpg3T1Bbq3Hnne8WVmNjkocesqmshLvv1tA0wcF+le9XHYWF80HLwKxJUFMFf3wbrlwiWLtVcs18jc/drrF6l8bKl+GZt9RqxWiJZPonMqyycmhCZ9TOcTiokYwHcWXyMGRg6AVsG3Z1ziRcpBFxCYwMbNqrBs3jbvjOF+Cm8ZWEr2+HlzYr8XbvDdAwASL0CB405opjeo+RKN2LbuXq7w7TOhaCuAAXDPS4+e+PZrhjPlR4Ju71HRwc/grRguB1WoQTiSOw3gMKBfjFk9DRo2ybls6D5eOWDAN52JyAcje8MHxUYF09F6bUqK9rT2LQnkip/xZHYMEM6OiFS+epwfCTcd3V8Pufw//8ObjDsGUUsn1QGrYZjlmkXYKYBlgaoIGU/PQhyW8eO77teKYc7oLn34TJ9bDssqPCctYsjQceEGQyEMvAw0/A1Ca4ZP7xrzM4CL29klhM3UePR0XOvF6A/hgYbvjMMpheA/s6xluMuqCuXtDSAgd74Zn1cFENrN0IA3GJPmiR3G1SWmeTzhu06TYZTUOOCtKaD0yJbknSGT+6pTEppFHIC7p6VUyOS0JQgz+sBzMJl01R3loBj/LCGk5MrMA6lmRfH4bXy9/+0E/rniIICciiymqjglTcxUMd8M1mFbXk4ODgcE7IJOSdFuFE4gis94D+YejshcYa1Upasw1uuFxNkH8sAAAgAElEQVTNHUUNKHPDYAEWhY9/3smE1RHqq5QdQ3uPqmL9P9+E6WeQMWi7oaYZEhlIZ5SR540Xm1gHEsycH2a312Dv9gLSFngDgsqKc19K8tQrkM7CG+tg1lSorjj6WEWFYGgEfvqEOv+9B6G8BCY3Ht1n0iT4zGd0wmGQUrB5G1RXwj2Xq1Zhc7kSVwBTauHKi2DrAZg/FYai8G87YbgIsoehyyWRtXk0d4H9e0wSS0dJ5D3kNQ3Ra+HuMZHDPtK6l1zGgAA0VQhurhZs74I3MtBmwswoNIRgTStUetR53LUIfrdaCauW93Aeq2P1akxfMev3XqncU31AEEgDWY2LLvaRsSFhOgLLwcHh/JDOKsIJxRFY7wEBn6rKJNPKciA6HmacsNQc0VdqIGZB2VmG9fp9cN/HVaswHFTi5ExIpNX5LGiBz06FJ1+BtsMurlgW5t5PG6zbINiw2c2aDRYtTYK//Zr6LesbUe2x8jOYMbJtGExCVSVs3an8qMLBd++XL6h9QwEYGVPfH4umCebMUV+/9Dr84hkYMeF/fAVuXvDOfeHGJWpbPQJ/6INeAb0BGK6AwmRLtdMsN8Jt0ftKKSIp8NbEyXQGsISOldDwR7LkhA+X1Aj7NUIuNRgfdIGegmELRlD2GUV+SOWgqhge+MiZ3f/zYdrNN7OxTcfyaFBmK9NVF2BC/Q06S66CWi8UO8HPDg4O54MWBLfTIpxIHIH1HhCNwN03wYtvQUkRLP8Q/C4O27JK6Fzlh2UnEB9ngs8LkxuOfh834akRWDcCFQZ8pQFK3/HH9soFUFep/KD+4xHVcvN5BYe7XGzcCdddozbQ2dINP90J7S+ALwdRP3xkCSw5zSzWizth1QEV8/O5j0Fl8YkDiavK4aolsGYzLJil2oQnIp8HswDtCaguhRc3wZLpkCJFgQJFFJHMKwHod0FrGg6ZkPFDegBMnyQwJ0GuzY0dBGuSQaZdIxqMU1o1QGysGDtrYNdIRJnG6KEyyoo8NIVhj8wxUlJgiWGw/k0PVl5QHVE+VG4Dbltwbu3Tc0F3u/F6obwIcgWNUR1cFty5TJnQXmXA9bXHG9Q6ODg4nDUyibScFuFE4gis94gZk9UGsC6t5q4aXcrp/NUUNLlh0gk8ps4GW8KjQ7BxBPZ3wUgSRgrw/WlHfbLS5OhyjeBtdKGPFWNZGr7x0OegH4ZGjh5vOAG/XKsG41d3g+6Gayvhze2nF1itAxDywHAKosUQPsnCNiHg+ivUdiKSKRgYgqefUvNYs6og2gCLWyBDhvWspUABvX8+b+wqRxNw1yzI2LB1FIzCePfMlAQEiCobUaxhajpatQEFk0gohm9BBpkwKKsfIDVaSt4d5ooyN/3uFNHKFBd5YW/YomSun9v9EYJuZQJ739WwoQ86u2BaFKrHRaRtQzoPQ0nY1QczK6FxgoxBfVG49TJ4eR0EXMo9fl4t9I7A5CB4nNagg4PDeSIF2M5nyYTiCKwLwJAFAe1o5p+OysQ7X+IW9BQgKiAVg/ggdNeollqlGwqYrGA3GfJYSCaHqqgqb6CzV4mrkTHJtDolZKJRwUPPwPpDkMnAsAnDw8oy4pOXnv5cls+BF3bAxY0q4uZc6OmDHz8GHWOwaw98eAmUjcK3PgolUUhhY2EjkbzZYVMZhLwFLxyAtTokR9T5mraNdcgiFfZgRXVkToecRLgsQjLJpYfWMlYSpqe6mtKQheHpIFlmsCtfRY/toRYbV0mSsugYiWqbDdtq2Ly3ElfG4MebNPSgoMIPzQ1KADZ4YdVG2NQG27tVK3FqDfzXPefvvm7a8NIwXH0FREIQS8HUMiVCr5kLLXXnd3wHBwcHAEEQYTgtwonEEVgXgHoXrExDVIIpVRWrfALuvHtcsNUXwdJpkKgF6YX/tQMaAnBDc46su0AJIQqY9Ouj3HN7AyvWwlgCitySt9+U7NgM9VM1Vr0tKK+A/UPgrYCrGlXFqfwMhrgnlcPfnCYnL2uqrcj77sdsG37wArwRU63MeAU8cRgun4XymAACBFjIQgoUOEAJ/WmV+TzSC8+OQC4OvnCMspkD0CAZ7i0llQkhPDaG30QzbCoH+jD2W0ypP0C0ZISUPo2cMEkVbNJWFqlJBrImPv8QFbqXTF5nW4dBopBgtD8KpkRLCHqjsD0PG0YhnwJ/Fvr6IKcBOWjvh21d5y+wDA0+Uwtv7oKhFER8cDgOD94CnX3qfSyOnP44Dg4ODqdCksSynRbhROIIrAvALA8sD8GKFLgEfCICVa48Y+wGNMLMQDvJW2HZsLkbChZcXKtmgI7g1+GGiAoBDkfBFYTeXqgsgu4U/LHXJNaQxiROGS4uo5FQEG5epp6/fj207Ze0dQraR2FKGRRs+PqH4bmDMLkYOmJqsPt8SGdg6z54og18IfjaEqgISJ7Km3TaNne5XPz7cxo/eBtyXYANwRB88iaoaYCfb4KvLgG/GyIUkc5DuRee2gw734a+TqARhM+mdMkg+bgba0An5EqQ2hNCDmmYERdaxESvzxIv8SPDNr2eKjJ4cHsL5ApuOpMl5IVJSO8nkfCT81u4RrwkR714Qhl0n4WVcqlMVEsJ5VgQRmNQ74WADmN5yBnK/2xW9fndtyOEXcoSomBBPAvlYXhuJby9FTxuuO9jUOnkFDo4OJwn9jtjNRzOC0dgXQCEgMv9ajvCMJsYZTPqz7RNEXNP+NyNnfDEDhAog9APtxz/+NIw1Hmg3yxgkeWZUR8pGacPixER43IqGCNFGosAx/tCzF4ENU2Cf/hnje37BAtmw+ypcPlkiw2H+nhzvYsbLw+xuM530mtr7YDeIbh4+tEQ5GOxbfjuv8DGbZBohqkzQCyBPLDZskhJyRN9Jv9+wCI36AZULzUZh9+thAc/C2MZWNtrMq9iBHvPCr639wrWdlXSvgWGtqJW1Y3YELKRl0gKWRfZeADdY4GtwRjIKFgFg8NaM6Z0Uzw4TL+soCLQT8oTpD3vIe/OYdoSXeTIm15SI16K0xZuHQo5DRICcsrkU3oAW5m9GgYM9kMwAkVxVYF76OMwZQK9sZZOUT8piTRcMQ1+/oSy7BgYUdsRgTUQg0ffUnmWd10ChjNT4eDgcCaIEJo2US3CRyboOH/eOALrfcIih0BHYmORO+l+OVPNbukCsoUT71PrsTno2U6MBFMmx9neXoUMmdSVdzFamEbCLqXV1ugSWR5w2dTrGvtI8xJjeMo0rru1BPP3LqZPho/dAml9Jy1VewjbEa6dl8HL7Sd83WQafvmMsqLI5eDaS969T3cvbNwC9bUw1AvFIXj2cRgeFiy+zSDWmOfhfBxRpgMu8FtQMMCGkRHY1gMza00e7hnmYHQ3O7eGWdNtI0c7WVy5iVWRy4iPRSEnkbZg+Ply9ItNrARkewMQABqAqMDnTqGFJF0D9Qy4KqgM9ZAvuMkKPx4jRzprUsh5GLOiZNMBLNtFSs8ye1E/29eWY5tKrRhewA3hAEzzQmMx7IqrFZfFYfjSJXBVy7vvxfngNuCa6Ue/v+5SeOIVqK+ESce0cPf3Qc8o9MVg2SxVzXRwcHA4HZIkJqvf79P4i8IRWO8TxSxEYiHQKOKik+63qF5VrvLWu/MJj2BhkSJLnjihSA9fn+Nis23yZk7wVmyILfkq/EaeaT74mZ3juz4vW0UaPzoJLBZcneOTVx/1dhghwdy5Y1w818Ikf9Jzc7ugJAIDo8qO4kREQlBeCh3datVjkYS2Niguhs2/Nmj+bykMW8OWOliAR0LOHn8BQWtOUGVZZIVkoFDBa/0VdG4M8jfXfZ/kmIuu+hq27Qkqd3MBaY8fwuNlmyKpfLAkYNoYXhNpCqQGhr+AFIKs7UXXwKUVME2BptnEkiXk8y6KQ2MUChrpcsH06xOUDhUYa4ugxf0ULLiyERZWwqrdoEmIeGBSFG6bdvw9kBJa25UB67RG/rSK83yYMxVmTX53UPbMGtjXA2Vh1Up0cHBwOFNs7QL5z/yV4Ais9wmDABWcZioc8Lrg5pmn3seNi0VMp5Ut+FF9uoCl05aOsjdXx3DCy7D0EXfBUMTkfm+OKcLNmyRxIajFzZBl838m8qyxCjTpk/jHUAcRfYyOvJ9/Tb3KUGoWl4py7qnQCI7/1LhdcP+dqpJVehIz0nAY/vV78OpbatUdOXj1VUimJLglBjCpQvJ2lYlckEB2uChk3MiQjphuE3MLNsckH2oIEuj10ro2TCFhMzAcJTumM0AJuC2ImjAKNBqqnwqqpVcAYkApWOgYLpOi4hF0bMyCB7cnBcLE787gcxeQRpaCpZNLhQgSImh5yJoCEcxRmdJw1Sbx7/ZTHlYWF8/3qwredY0qyuijM6DmHUPnG3bC719RreLGGvjynRPjo3WicYloEL5w9fkf28HB4a8LQRCDpRN0tF9M0HH+vHEE1l8IFRQTZBb7aaOVQ4wKG1+hidhYCdIQ2LpGljwH43l+GFrPlV6Ny6hhGk1EcPHNZJanrSR5w6TddjMSv54fBHr5UWoA3Xah+Q/zZDpCocvL1xrEnywnvB61nYqSxjw31Vr09fjY2wuBGTb7rTxVy0ziUsf299E0O445B8wuHTuu099RRX1dO8uiK2jwjjCntJLXn7+NfCEMGckvf3knrqiOGQaWjVe/ChJqAa8NSQEFoSpi/RImSzyDaSpaeqgo7yefrMaW5XhdNinNxKe5iVaOMpwOE/KMURcs0JgrpbbcwgjatAmb6X6Twb1B5s1QVamVe9RM1G0zIeCGrlFVxXonbd3KF6ykSEUoFUwlTo8QT8OeThVoPavBmZtycHC48NikyLPm/T6NvygcgfUXQHccntwLw6MVuEPN6HVbCfs9RDwWo3YIWbDRemzyaR13kcWaCo1KaTJJ7KOIGClZyU7GqHKPEZPFjIkSslaMA5kkmsxQ724nVWgmYUgOpSSxgiA6bp0gpfJqcp1EFIyQ5Sm7nZUbQ+TaBf0DXg4kwFumcV9Q0ClNGvWVZEs9HDzcwIgWxKy0qG85zEd5gpQZpCdVzWRfH5OqHqZ09hfItnkxfDbZUkE+FIU6CZsEzAIMW7nlRSTEbFW9GtXgxxbN8/ZSUhSjZJmfsagPocdp0nw0k6Mdk1HgopCbEjT2F6WYarvxaYJRDK4lRLPp5X/v8bIlD24vHO6FkAv290B1sboPjSeIL5o/HXYeUOHfC2cdL66yefjpS8rk1bRgSQvcfoJZtveaWALae6Gy5MwjmBwcHP6ysHBWEZ4MIcRSYIqU8hdCiDIgKKVsO9VzHIH1Z07ehB+shcOjsL4dosUzMVsn4/IVCM03iLpsrBFJNmUi/BaJkSCHE+X8wh/nJquXrL4bKQVR7VKGTB8hPYbPzHAfj1Eh09R7K9kjp6FpKTyM4ZFVf4plsWz4xS7oiMOnZ8CUE7QJU5j0juh0dnlIDglGE8p5vL9L59WNNouuHsK2siTylchSAQGwpU6puxcjb5HW/IQjJj6/F6M4yZxbt7DmB0sR5YJsWxAaBGR1qAQooBcVsAsW+EC2uiCmw5hNbVMH9TOHiFilPJCfz9O+IWyy2PQyAx0/OlsYoxQXOhFusErIJr24QiZXaGGm4WXzNkFhCxzSwF8JzcVqWzAZRtPKZHXLdngtBh+/7uiqyikN8M17IJN9t53CYAxGk9BYru7n1kOnFlj7hpWYbT7P4fXt+2DLHpg5GWZNgZ88AWNxZfvwtbsdby0Hh782JALpCKwTIoT4HnAxMA3V/3QBjwKXnep5jsD6M8a24bFtsKUbdg2pKshItgzhyYEvj93txlVtk8EGWwAawmsRGwkTHw3xc6uEancX/mAG4Y0xV9vDkFnONM3FMiPPI64wKRkgJ1yM4aUyK7miSBAa/6lJm3BoTJmnHhhTf/gfP6CqOndPhSIPVOOnMRfFlfWiSwvTSOPTNdzCRSYuWGp5+dHgFLpcxWiWxO3Ok9N1dMMkZMQYtYqo83rRBRgNOcQgGEUWenMe7bCFnSnAGhf0AQMa1q0uRJWGHNagXKpfA0sj7i2nt/VGGmuDVLttSkixk0HmIfDhZxoS29LZnx+mqLuel1+PohU0rpgFLVeq69V1CAhYIOHWxVA9BfweeGUbHOyEzh5I9IKQcM0Y1B9jWxENq+2dFAVURWsgBpk8TKo8+fs9mIafbVfmo/9wCQTPMWppaBR++4IK3N7Xpl5/LA4N1dDeAyMxR2A5OPy1oRHAzUSVz388Qcf5wHAbMA/YDCCl7BFCnCQQ7iiOwPozZjQL6w/CnCisb1XhyEZWA8uDiUG1r526SDvpIoNeVw2xRBQZFKTtAIbLJjkWoX+oBk91Bl9ziqg2zD/IR5nvuZeMrCRPhiHK8AgTTZPMDoa48Zj2UcgNt06GzgRcUgWP7Ff/vycN6/rh+nrQ0bguXMKLeopeGaMobDCU9ODXdB6c4WH3wQo2JS4iEu7H5S0gDIlfz9KTr6Lg8VClJ5jiFQRkDMsboCdajWdJhuQzQYx6E1uzsTp0ZI8BxUBCgyGT4JQkVrFBtsKHrBZU+bzMLnOxZAq0+rIMoVNBlkGyZPAj8hp9BzSEMOnbEmVLq0YgAN3/P3vvHWXXWeZrPt+OJ6cKp3JQqZStZAXnJAsDtgGDgW5oTBO6AdNAc2fupXvdO3fumqFnBi63uT10Dzk1YGyaYAw4yTa2HGVZOedSqXI6dXLY4Zs/dhnJ7lJwlGzvZ62zVLXP/vb+9jn7HP3q/d739z7q9WbsaYOlS73rcxxYtszzv7Id2DsAnfVwfALWXw448PMnobcFbrn0zAnt0RB8Yj08sRciAbjmotPvGzdhSQMEVK/B9cvFtr2KTtPwlnijIVi+ALbvh+5WrzG4j4/PWwuXImU2ne9pXKjUpJRSCCEBhBDhcxnkC6w3MCOTsGOfF/kIV6EsIRQCXVg4RpaFTTupCpOoBtr8PpyqSn4kjlsxqGQU5KhAhiXWUAAzWeFwej5bA49zlA1cp30K3dlByTbI2iESIsk7U3vYJsGw5tOlpoiqsKYZVjdJXPc5Vqb2sS9TT7ZyDUkzhIPLcUbJRQv0XnKEJx5eQiEbQCJYsarA8vlx/vF+hQJJcgM6oUCJULKMElNYXb+NycQlXE4/aTFBO4t5Sl5LsSipxgKwWkPbWqVyVENm8ZRCkwBb0LhmjMS8DEadRb4QZbIvjdGZY2pxCdkZYR8B6tAxaGWE7WQI0T9gUtKmqa92oTcpTLRB3wDsH4Xv/xa60vDZD0Eu6uVa2dL78GgqXNoLTx+C+qjXlPoTX4M9J7xIUzIA16488/vYWgcfvPLs77ehwm1LXvl909QAb78SNu+C6y6BrjbvceOVnoWEb+bs4/PWw7O89j/8p+EXQohvAwkhxF8BHwe+e7ZBvsB6A5OMuCxst9nSp9EQVeg04CNXQDl1hAPaRtpTR6iqOuNqAxusdbghQErcsop0QFoqblaghGooqktcTJMYzdI+vJfBFoP3tyxnjTJMxZiLIY5Rc2P8f9k2+pUJOl3Jl629tFd24YYN3OghLq5P0RY6iBQDtMc/yQ6O0McYJVnmREueriuH6d/diJiyGR9W+dgDWabybRStAFbCoFIOUVML6DVBOLeWv0u3YgoIzNymq4Hq4Tx6sIjS6VI9EcCcdCkPqBASEAIaHJLLJ1GDLjXLQDNt5i49yKLlBvVxnWmlgglMoRMgic4ClNo4o+USdU43ZmY+QoFLl0F3EzzwGOQdGJqEJw/DhqNeRMp24Lp5UKzAeBHGK5BR4YdbYfdRmBj3cqoe3XR2gXUqY2OeT9jq1a+t0Llqlfc4lfArbInk4+PzxkUhQoBLXqWj/fOrdJwLAynl14QQ64EcXh7Wf5VSbjjbOF9gvUGpYrMjeYzL/qLM9XaA+FA99QGDxS0qG5XtNLmPsTM3n6wWI0GGVnuIfWIBOAI1UiWpT6MFXMYH6zAbyiyLb2ehtoeeo8cQBoQP/wHZ2Ey7plFlgCkS7K8lOaLpJESeA+T4Vm4PX1SKpMZ/izBWoweTtMWSSLcfKabpZ4w6ouzM62wbD3H8WCMTG1oQ0oVe2Ou6RBwbGVFR8i4oKrmhFImShmNBuRfiDd71PjwKDw5DsBSlkAliV2ycbhe734R6AQrQKtF7LNSQiyM1LKnjagptvVn0RISY8Bw+52OyGI1RyqxkPkp5KU8fhMCLkvSjMa/n4DO74eKFsH/w5HOHyvDwLnhuC6gTUFNBzUAsBFNlqJUhEoTMIC+Jbdvg0cegt9czY/Xx8fF5PfCWCJ8939O4YJkRVGcVVafiC6w3KMMUGKdEmxHmkHECtWcnqWqBQ247hmISVDQ6w8c54M5DFQ4RLY9mOQhHEtbKRJU8WRJE64o0mYOkrVEUKbCiEeLZDJVAhG8cX0VGWixt2o4bEYwogrIwKVsWg7l6vpW7iZ8beRY48/nfjt/BygWTVGQ3GjpBggQwmbZqbBg2GbGTTD/QALZAmhockDiLIacLkgEbrSuD6loENYUFW+pZGNSJRbxrLdvwyCh0hmFBHTRoGoVdCrlDkomQoLpIwuUC6sBxFEq1MEgQMZdoIotR74CTolFTSBNgPiGeZB8VaXHULbMyOAdlJip1qgfV8VEI1sPalbCkFU6MwFXLIRiEhyuw7yAcnwQn57lDdNVBfgICNdAdSJTgkrOYxL6Yq6/2cr18ceXj4/N649s0zI4QIo+3igpg4JVPFaWUZ+yX4QusNyg6CiApY5GXGVaO7qUjs5+JQAjZei2qUUeTPoDrHGZINjOh1qMJC1W3cSoqxUqYQiGGhs14Mc2ByYU0hjdyfPF8FpWG+PrwF7i32EkFg98cWcLb2x6nuW6AVmHRV2nAzFg0hwd5e2wDUXOaX4gllCp7OBioElfX82fCYKGs8E8jQ0wpvVQGg9gF3bs1gxLK4JYVEIK4YrN8jsYaXUEGa6xfZLFE0TFmquQ0BYIq5G1oCnhG7TevV9jWJjl4Ao6aAssU2FWv1cN0NcXczn1oUZs6Q2GB3YmUrdyuBlGEwjRFyq7FvqzG4+USh2qwtgcePwCdqZNLcyMZL88t5EBuClqboD0GC1rhoV2wYxpIgOpAecxbYhs87K1UasDlK+B9M20cXRfu2Qi7DsMNl8Ka0wivQACam1/LO8fHx8fn3yMIE2Tt+Z7GBYmU8gUVg0KI9wBrzjbOF1hvUFqIcjEtHGeaFXaU5sxO8sEGWmsmWqHI3lQrthOmgSoTqs4lcisj5TYG02l2jy2kkvEaHMfMaSZyaYQjKNtR5gZ3U4008Qe5jBwBwkqJhmCGE9UkC9xduAqMZxK4kTyBgsO2yipUzWFu3X52aA0EsxqG3MtEdCf/dfM87nroZpweB1cVM5YJAmoSUDzH9YogGFZxVUE4alEDWjSNUx0IdAVu64I/DMHqOtg3AqPHYUmL4JLl8MtjMFgGHMhKhVymgUPTQVKLM0yYOoPlCD26Sq5VIWFAnBAttU6eKuaY57ZxuAhXdYHrwNNHPAEnBRQqUGd6Vgx9Y5Cx4Y7noOUwrJ8H9wgIClBDoAcgEQQlAvPqYUwBOwCFMpgmjGfg2T3QlPKE1qqFJ4VcsewJsOg51aX4+Pj4vPq4lCiy+XxP4w2BlPJuIcTfnW0/X2C9QREIFtPIYhpx1Q7K2qMolRKmqxFmCYemoxx192I5AVqTx+jRK1STR3DtQe6pjPLIxA2oFoyUWtGNCiRttmqLiDofYZkyl3h6DHUyTIgqESNHzkoylEsxJzbGxrE1jGnNRJVDGIEaLgpbhteyKvVNVvU/xZwcbGqby72P30o1r8EeHbHAgg4H+oXXgLkBKCkocUm6WeM9epAuFLoJ0MS/N3jqCMNnemEoD3/4HRwYgxYXbrgJ3tYLm/OQVKFiwe4JIBOh/4Egcr6FogiqwmBXAK5s9l675VozjxWaGbK96FhDAHqWw5XzYDQHh4YgOwzNOqgaBDUQSc+OYWAK3hOBTy6Ge3dBZQyu7AClCm298PgYdLVDKAoj416LnHgEUjHoH4NwHO7ZBM0p6KiHH/zOW578zC3Q6C8N+vj4nCf8KsLZEUK895RfFTzTUXma3f+EL7DeBChKgHDHFyG3nU0hyYmQwtPH2ukOqdTECGZlCSKSQTDBIq2PjvQesvkY+zNLiYgaid4pJuL15Nw4B6qLuELfRyxQRsYlalmgaQ6KXQJpYCgOhmZhlTQyIoVEEDRK2LqKXrZQHQ1bqbL9+BxqFc1zUg+A3G4SqC8i2yzsORqONEGDUEqwMA7vDZhECJzxOi0HfrQdqjY0JmEw4znY99TBXAP2ZmGqCoEy9G8BZ0pFVrx+hccD8MudsCwFWy0YtmBJIzQjmQhXeEp3uZog8ZDC8BRs3A3BEOzuh1UdML8X5s6Dux6CtgTs3AVTe6F5FBJJr+LQlTAwDp/+MDzwBKTrPPNOgMkCxOuhrwjP9sH3nwWpwLUXQavt7VOsvLb3iY+Pj8/pUAgTZvX5nsaFys2n/GwDfcC7zzbIF1hvFsx6aLieUbZgU6U7VmZoahEBdRFvS4DBBLt4ghiHSQRD/N+L/gd3u5eTkSk2s4ZiLURKmWRC1rOr1sr7gvt4LNzIZK2BghWlW+1ndeQYCVQWtIzRt2MOffYcGu0RtHgNtw52s5a1+n24oTI5ux67qkMJmHE0D2tlpCNRJ23Gk80ohsv8qsJ/SUNEOYMb5ww1B4oWXLkUdh30+v/dfhlsnoTnNoKe8czbWxbDsSqggawANjg6HAnA5zdB81yIq5BzocV0mTLKOEja0ViMyXTRWyZ0CqAWoDkKn7gJdu+HdBWKA/Dzg3DVJVAowmPPgS4hEIZ57Z79wfM9B9WZpPkN22FXP/xhH+RsyFdBOrCtDz79Ic9Dq8vPvfLx8TlPOBTJs+V8T+OCREr5sZczzhdYbzIuZz6jZOmsS1GMQkiFiAaTqLgkmSJNM3upV2M0iDx5GUPYLiQkfgcAACAASURBVI5QcVHQsKnIIHFtkneoGVxzHyEnQ7s2RoNqcJFcyS8mlpNQNMoVlYFSG0wKlHGH3yRu5mDvStZFhkhNRZCBILhAFQhCZiKBaVSxVQU0lzZb8KnlgnTg9OKqWPIcxzXNcy8XcdgwAekF8Ll5sKIBoiXvz4muJbD1sCdkAikXUatSQcERKilFgaTCU1PwgTwUXAgH4AQKc2wDdJuWmY/Dwja44wF4bit0tcD4KGx4Alob4XkdqCheb8FDh0GpwZ690NsN7/2I93zwlGCcZcO8VjgyDBXFyylTHa97kVuD5XM9SwcfHx+f84d43ZYIhRABYCNg4umQX0op/3chRDdwJ15fjq3AR6SUNSGECfwrcDEwCXxQStk3c6y/Bz4BOMDnpZQPzGx/O/BPgAp8T0r5/8xsn/Ucp5nnNzjDUqCU8vNnuk5fYL3JqCNCHREQEDa9bTUsnmMnBjplLqZCJ2meZpHI0uekuZzHKYswioCAWmZuboCLD1aIJJYz3b2cYXOUCHMRKGwudXJ8PErQNXCrChQUKNqEWgqMiBRBbZoRq4N4LE7HCptDRQN3FM/g1NQoGyqEJN2a4F1tCu/o/vfiane/1/rn4UfgeB80N8DX/hPoKXDC8M4IjFShOw2Hx2DfGEwWwZwAU5NMNVTpuPogxSyM3d9EdnecIVykZWDPhW/fDVGgIQ3NiwUr1Qh5CzYH4JokxMNwaTckJLQ0Qq0GfYPw7uvhSAa2jnpWDA8egFwR3rsOGpJwfBCqVSBy8lr6x+EnGz23/eZ6WLsEDh73vgmCOtx8tS+ufHx8zj8KISKsOvuOrw5V4DopZUEIoQNPCCHuA/4D8HUp5Z1CiG/hCadvzvybkVLOFUL8GfAV4INCiEXAnwGLgRbgISHEvJlz/AuwHhgANgsh7pFS7p0ZO9s5ZuO5V3KRvsB6C2Bj4+AQIIJAkKebi3gPveJJLlO3U2ffC4qD6VSRAiaSDQyEdCLPPoM2ZWKs7CGjjTNfdvOjPhO3qmM7NewJAyou6BqFvXGMRI3C4hDV+iAlN8jya0ZZFGtj11bBxJRAKIJwAla3QE89XN4GrTNiJJOBWAxGs/DTJ2BfBv64DdqDUOqHn94Dn/0EGIr3yYybsLcfHt0FhgY0gqWCXGYTT2YYLZsc/3U3Mq9AUkBeMLzTxZirQAvEKjA1DeUJeDwEXRFPPOUdeF8j1DXDc9tAr0HMgg9eArtGYWcFmlqgSwPNgEMC4lGoWd6yoml4lYOHRiAWhH97GnQN4iEYmYbVrdCa9tzgYyH4+LIzv3dSnrmXoY+Pj8+rgUuJHNtel3NJKSVQmPlVn3lI4DrgQzPbfwz8Nzzx8+6ZnwF+CfyzEELMbL9TSlkFjgkhDnPSPuGwlPIogBDiTuDdQoh9ZzjHbPP88Su5Tl9gvckZY5gpJqgjxiRZFFRWshgFkySt1IlD1Lk2WnmS6WCUjJLCRsMKquQvEhhPP8uG4Uu5buk4U+VBCpNzWZqAB/bEUF0Lp6ZCRYCiUBvSmdpRT+qKCSzL5qha4tZlFl9bYfDAcdg+ATsK0JaEW3rgkhZPPGzfCXfcBZeuhYUXwyCw3YFKGEYcMMdheBQSOnyiHfblYW4YfvoItKc8gdWSgIEM1DokxZxK5mgdckLxMskB4kAvOEclZrcgG4H2AoRL0GZCTIOwCrsKcH0MnslCZy9ks2DrkOqCBw96+VihmSLHFd0wMQU7ByGtw7uuB8OAL30XToxCXb33DVJyvQR9U4Nr4/C/XgHZKjSFoSky+/tWqcAPfgG5PHziz6Chbvb9fHx8fF4NJAKXV+2vuXohxKnRn+9IKb9z6g5CCBXYAszFizYdAaallDNlPwwArTM/twInAKSUthAiC9TNbH/mlMOeOubEi7avnRlzunOcFiFEA/AlYBGcrMaSUl53pnG+wHoTk2OaXWzFooRNlTWsI04d5owNQpYBOqs65YpE1RyyWgLNsZGO8KzJNXAXOCzesYHWr55A69VJmxodc0z+yByEKWFA8Va4XQGWIDuWQu5QUBTJlFTpj7rMWQmfSXj2BvfthvEc7BmEsAJLm8BxvEiN43i2BY4FlgaxxRA4AL1dnk2C40BH0HtI+cLojgCQsNRV+NdCDAcLVOHVe0i8OUbA0V30iIpegdUhSLRDbUaDTVmQNmA4B1UHVvR427MV2D0OuQp0ntJORxGwaC5c0Q5X9Xh5V79+GJ7dBgEN+vtAaYZoAqJBGMvBRBm6E6e8R0U4POBVRbY1etdkOzA+BccHvGs+MeQLLB8fn9cWlRAxXkLj1DMzIaU843qjlNIBlgshEsBvgIWz7Tbz72zKT55h+2zJZGfa/2z8DLgLuBH4NPBRYPxsg3yB9SbGxqZClhLjWFgcZyerueFPz2sEsK08TtWgWg0hq6BYknC1hKwPUbCiHEnNZ+GR/Rysm0dET+GEmsn1H6M3Xs90VWBbBgSkF6ppkjhCw6oYRJuy2DWNH51waZg7xXUixD98x+Sx44JaGWQRRA2k5QmL226xMNbn+JlZpv0amPt0jK7GAGt6DSYdm6eqFb5Zlrw/FCKtqAgB6xbA73dC0PD8ry7phq89oVEeVGDS8D42JbycqLlAFcJdklXt0KTA7R3gaPC7CTjuQp0BtzZCoeBZLrjSE1ElC+akvGT1TBmSMzlTUoLtwtz0yaT2gWHPrH7HJrBrcOlNsKQbciVIRmHDfog+BJd3QioEd26Azcc97601y7wE+OkaXNUBa1eBVYX5Pa/nXePj4/NWxKHENNtf9/NKKaeFEI8ClwAJIYQ2E2FqA4ZmdhsA2oEBIYSGtyYxdcr25zl1zGzbJ85wjjNRJ6X8vhDiC1LKx4DHhBCPnW3QayawzlAlsA7473gKswD8pZTy8Gs1j7cySVKkqKPIKC10YVNB4iJQqHAUwRNMGn1Eh8ZBkUStHKVAGE2ziRSLjCn1hHfk6Nm7l2NNMUp2lF3hMANuHf0BHWk4KPMtlLyLEnZQ11axJoNYNR1ZhdqISa3s8NWHFf5PA5w6F6YEDEoYF0ipgHQ4kXX56t1lxAEVJ5gi2OwST1UZK5fYkw0xHa5iri7yEx221Ap8y2zEFCpXzoOGqLc02BCFo3l48lkXhvGCuPVAAi+KVQBUi/pgmSviCYYqsGHSa8wsJVzeCDe2eYJKi8LyFtg+BJqAqAnXzvGsFb7/LBRrYKqQr8FFTdB9ijnogm5Q7vO6AdnCq+BsTkAw4fK9x6Cqw1eeUFh90DNN3T0Ebsb7MOx7DHo6YNF8ODoO85rho287DzfOK8Bx4P4NMDkFN73d76no4/NGwVsiVM++46vAzJKbNSOugsD1eMnnfwRuxavy+yjw25kh98z8/vTM849IKaUQ4h7gDiHEP+IlufcCz+JFqnpnKgYH8RLhPzQz5nTnOBPWzL/DQogb8URZ29kGvZYRrNNVCXwTeLeUcp8Q4nbgvwB/+RrO4y2LQGElVxFEp0yeOSxDzEROs2xkHwa2ZhEMOghd0pIdRB93sXUNN+ky392DlTbIrk2x/P6HeDYcIJsqM2o3UM2bqKpNz+r9NLaMMXiig5pmMFkyMbQKIbtCsT+GPu2gtdlQLlIKh1Gulbg1AWMGRB24xAUhsA4FoSgRChQHJCXTJJSsoNoWQijMq9cIShgSNqPYdMx8ESxo9h4A37pzJtJbwFsSLAJIGHXhGNAKg8NB7lBckgGFQy4si8O8kOeldVMb3JOHTSUIpOF9zZBSvLyroO5FnD5/BWwf9JYNe+pgpABf2Qi6Cut7YN0lYFlwx69gTivcdhv8fJvkVwck01moqgIUl0djLrau4CYUry9jnRds6y/Bp5KesDs07LXQUWYLdl+g9J+AjU95c65LwY1vP98z8vHxORdUgsRZ/nqdrhn48UwelgL8Qkr5eyHEXuBOIcSXgW3A92f2/z7wk5kk9ik8wYSUco8Q4hfAXrw/pT87s/SIEOJvgAfw/jf4gZRyz8yxvnSac5yJLwsh4sD/AnwDiAFfPNug10xgnaFKQM5MDrww37mE53xeJiZBlnPdnyJXz+PQRJkBEu4YoqRghsp0DQ8iQg4EXeyyTnYqjF3V4N02vzZvRt8BO0LzcMYCUOcSmpcjEisRjefo0I5wItdJY2oQewzyB2NEjCxT1TRasEohmEJLOETaswQ/X8R8R5X+jXNx0zrUgKUShAI1iRJycSVYIQUXFQWH48MKbZEqPcImIWsgzH93rUubgbIES3hRrKoLQ1Uvn0x3ISewbUi9Q9A+z2CyCluznuFnVwQmbNhUhnYdxh04ZsCqF/VKrw/D9TNFwBsOwaPHoC0Glgs/3wkfuxhuvMZ7ABwZADkJ031gZQUEQEQdrKiNM615y5chxfuKyUA5CJPjEIlDJPDGElcAqSQkE15yflfn+Z6Nj4/PueJQJsOO1+VcUsqdwIpZth9llibKUsoK8P7THOsfgH+YZfu9wL3neo6zsElKmQWywLXnOug1zcF6cZWAlHKTEOKTwL1CiDKQw1t3nW3sXwN/DdDR0fFaTvMtgXhRzl8j62hEMinyxNQ+orkcAK4icHSN0VoTmuqgjbukG8fJLzX5zTMfwHnG9N7NrKCwOcZ4oIHmrgHMhirtDf1EnDwyaBPcYOHkFEbf3cJAtIPKWIhqKYiT07BTOom1GerMUcYPtCACElmSkFGRQeHdlRKwHQL5Cu3Nx9BVi+vdUT5ROUJMFiBwDQSv9UTZDLeugNvvqUHZ9Nzjj1ehXPV+DmlQsKHOYN9vS/zN/zB4Zhr6CjBW8+wYvnUQKjGYFFB0vejVmdg84IkrQ/PyrmIm7BiG+Q3e86UK/OR+KNsCJSuQeaAIathGDdvIsob7fOC5DEID7SBsUeCilfDRa17hm34eiMfhC5/xvMPi8fM9Gx8fn5fC67VE+AbkKSHEMbxE919LKTPnMug1FVgvrhIQQizBC6u9c0Zs/UfgH4FPzjL2O8B3AFatWnUuWf4+LwENnVa5nGp5B9PP6aS1MjKpIEsOekaSYhqpCmTKQR11WT32HN+c+sKM/TjguMioSmlXiDmrD5IPxRClGk13bWbF3kPEo5L73n4zyhxJzTWwqwY5RSCjEA4Uiek5aktMsiRxcxpCk6iFAo6igykg46IXqyzp3IZwFCLFEku0I+xQ4xTsKEvLG1CUMARO6vOYKeieY3HsmOo1lC5VoTQAZQHFNjAdGHfJjzj84Rdwyzu8qkVLQoMJmRoEc9AVhQYNrgyffL0GqQGSVk5GznTVS3J/vjW17YJxyvdTqeIlxkdMCAqFQACqFjjDJoEJGz0gcbKCas5zdE8UoKkAb18Jt74TRjKQLXrGp28kgkHv4ePj88ZBJUSSped7GhckUspeIcQavKXJ//z8UqaU8qdnGve6VBGeUiXwDmCZlHLTzFN3Afe/HnPweSF9k8f4Y20LIdVBvvc9NO16gODoTsz9VdR2CC0oks0qlEsucsIgsWcaTS1TdSJeblMQ0nUjfL7r/+XK3EZ6jx1gx8MS7ZE8yVCYQkMrWgwitTxNDKHOsyhWgrgBScKeJmKUscIaoZ4KI9kO7IKC6HDQ0zlMzSX3XARnh4bWapMrJomXakxJyTxH4ZAOzW4z6cqjYK75UxSrAFzXGeUHqyrIPwrQBQRTUHM99eRKuLdA68Iw1RJs2QrTc6C7EaZsiOtetd8HY6CdEr0q4vBvTCCRfII0sZmPzboe+MUuL3Jlu95j7Sl1K6kY9LbD7qOQVMBMwjSgC4X3L4ly/CA85UKtEyo2NOTgxm64/BpvafBXT8E7Loa181+nm8LHx+cti02ZCXaf72lcsEgpnwWeFUL8X3iBoR8D50dgnaFKIC6EmCelPIhnY7/vtZqDz+zUikV2/+LnTC1UqdYKNK2O0b3s00w8cQD90JexRzXKikv/YRcm4ej2IA2hMZamf8/Te26DkIpRX+PGut9zReMTJELTKCXBRcsrHBs1SD9Upv7wAOlrhwlMlAk35ClHIFkrUQ3FwZaUBx2uDD7CDQf287P6D2N1pUnXNWFInUMWHFhscSJjUCxFaWwapzFZ4+G9V/McOsvbDuGEcmCP4RV3eFGlX56Ajm7BVUeCPNZlQcGAKRUO1TwRVq9A2qX5+hi97ZCOw7Zh2CUgrHtuE7ekXyiuXAmmUJhDABeJQpUKVUxCrGwVBDTYOeJFri7pgOZTcrYUBf58PTy+CzrbYcNGyGXgo7fCh66GuyUcCUJbvdfXsFUDPQJ37oVEAD58PXT7/lc+Pj6vE/J16kX4RkMIEQNuwYtg9eD5dp01j+u1jGCdrkrgr4BfCSFcIAN8/DWcg88s2KUSkcECnc1NTOGyvHIL+372e7SRMWK2zvRYBHFPmcxxG6sMAQWs/Cg3BL9GV8Mz3F/8e7S4oGv+MSKNeUbNBoYbGzHcHPWxPsxSFdsts/Thbey+YhlOySVsJqiOCNbuvhNtdIzqk4OsMY7Qe3UrH+tZw0PNkwTmlGjvnU9alcw1bQ7flCU23cP1zv08O7qUdLBM1XXZN9RNY9fToCQ4uUAHGcuLQnV0QNOYxrjl4OQUaFe9zPV3a9ABW3VBO5DWoDwGSxfBeAnGCzCegYeOwaJm+H0ejtcgpQnen0xQM3awg+OApJ5WermYRWmVRenZX+diBX62EfrGQFUg73rpYcfH4L8fhQWL4CsLYcMQ7O+HixohMnM5IwV4ehh6G2c/tith43HoikNXcvZ9fHx8fM4VlSApLjrf07hQ2QHcDfwfUsqnz3XQa1lFeLoqgd/gqT+f80SooYGua9cR3LKFSy+9grgVYnB8HNcNYTQsZCSaIbrLJm05DDuCrCxTyVW43Myw3DzMmp5H+YbxUzb3XczCyF5QJdlchGOVS+hb2MhnLv4BC3cdoP25QbQJh331zQyUAxxpWsBoNE1vdpSGaj/jY5L9jw+Tyv6Qm5b8DUeObCHRPo/GgMkNxOnVQgQCgmpWcGSoSMzSKUqVCBaaOwKhD3gWD67kSEkQMWDLBBy2JAs6BaIhwHC3660dpoAQUANHuDwYUSgPQD4CturiSJuRaYUf7Vb4QVmhrhuuvwK0Mmydgq2DU9zcM8iiWBIETDBAnAaa6D7t6/y7zTAwCV0zIumdV8DQOExJqKtCNA7XN0JXEH6Yg5AO41mvd2FjGA5Onr4XYbEGDx+Dlc3nX2A5EsarUG+8MPrn4+PzxsGmwjh7zr7jW5M5M84IsyKE+IaU8nMv3u47ub9F6Vq3jq516wCQrkvnDTdQK40jLwtRu+PXbLrCoKYOIPuqZPeCaoBiKLQuC3KgtY3CA/Xct+siUt+dIhKYZvOBy5hQ07goPLLuffxL40cJRGtMTNUzrYRwp8uIJpf20T6q45JmwyB9s0ZksMLEoRN0LJygadQmUTDZo0Y4DgwIm1WqTmP8g3yg/VHuHjAJKw4fbNkHoXeTNzt4uLyBHw7F2TPdQr6WoiuSR3QVmB9U2TPQhNBUpKF6ifkSb0XRhQKSfY02i+Y6PCIqVCZNHKOGltCo2SZHjwrKFjTUw9gxGJ6EnZ1zePvig7xtxSSmEqE840IikYgXdWDIFmFXP7TXn9wWj3mPkWlYnIcblnjbG4KeK97REdjR53X4Wb0AkqHTN3qOmvC3a09GvF4KjiM5csTGtiVdXRqh0CtTRT84ApsysK4Z/vys1ns+Pj4XKq6/RDgrZxJXM1w+20ZfYPkgFIWO667D4QQj7CR3U5Dw7wZZ8dEaB74BtW6QqiAnNVIrGon3BImpOfLPNrJt80oal4wwRT3xxDQDQ+1MWGm+X3c7n+V/4i4T7OheQ6iY5+pH7kAdy2KmFHoXt+JqAXbc1sQqawei+Ul2L76awZAkKR3ywmWP6/KYW+NjapAF6ZvoHSyRMKu0Nl1LlT+ysfbP/MvATQyWQkSUIcYrAQ64KpfUHWbvcAcN84eZnm7AyoS8RernxUgN0B0G6gQTlkSYoDoSB5dyDpwBiZSC7X2gJsAZgUhUp1on2Xy8jqXzTqBHxugWSzjEg1hU6GU9OidL53JlzxVemUUgRQKeyHqeeADWtMJPn/N6ILp4S4TvP0u0vi700t9r15XcdVeRXbtqKAqkUiqf+lSUSOTlfbFmy3D3fs/LtcXkHLyNfXx8LkQ0gtSz5HxP402FL7B8/oRCkn4GyMRjdKzSkFWTUkjDurqZsUASbbpM4fKLONjYQ9s7Brjc2sSO2mImx9NU7QDZyQTjuTQYgi2xS/jeR2/3epcjSY+O8nF7M4VcmHtu/ADWjqcwtArDCxewzahHyQWoduVonPgn2OhyfO07cToXMixsvutIvuRE2TkcImqGWL+kD0tuZGe5m4yjQM1GVEEdcymGwpSbBTHtCJHUQo62KVhHJMgZf63nu0I7YN8PhZiOMkfCFhW3okFV87IHNQlxgTMKLHUpPBJlLJCmqW2KfSJMvjIHXTXpMSZwcahReIHAigZf2M/wVIoVzx7iVG7shfoA3LXVE1y3rYG5pyS4j47Dz++GfAFuXg/LX+b34Oiow549Nbq6NIQQ9PVZ7N1bY82awNkHz4KhwpoA9Ffh/c0vb07ngut6FZ6RIGi+VY+Pz6uORZkx9p7vabyp8AWWz5+wUBjEJBNNI5cso2FfhoF5BtNZhWrZoKXVIdOeYrh9PhXHpPGzQ3xx9BH+be8HOLx/HnkZB0PAiE18eZapaIoWbRiB5GjTEr4393auHnucgFElsriEVKGzbpJDwXnUJycpqRpL7J2YWAz3JXiopYvlRpCDWAwbFl+41pjxmRJoCIoyiiJqhJQaVcukWg0gNLBsjXh6jKEdcbrrFfoWVin3aVDQQHdpUGD8AF5O1lYFd0xFSTtwwoAxiSZq2G06GIq3XpcHFCg5ISbLPTz9TCflAcH+ZJJvr1tHXcRiKlfPsA1zZ3rvJcKwuAMODEDrKULJsj17rjW9L3ztVQUu6/QeABMTkMtBbKYq8df3QrEE8Sj86g/Q0wXRyEt/j4Xw8rqeR8pX5hYfNOA/XeU52cdenkY7KzUL/vVBODYCDXH4+Dsg9gbzBvPxufAR/hLhy2fWZA5fYPn8iePsxsBEKIL9oSUoxl6cP++hNKYDUFgEQ41dFEWYpvIIE5FGng7EWd34BDIteXroUqaeSaM022SvjNCsCUq1MJbQiOkFdi9YTAtDFGQE29RQFYeAWiMkKwjTIe5OYDcpNK2d5Ar5JP2FdqYSV+GKAJNSsizqzVPKDjTeRldolIpIMmkHyWWiOCGFaCRLMpCluHkpK6sxsn2COiko1btkpx2uDQi2JmB8VEAfYElEg8Qd0qGgQMmGEwI1ZuHETC+SpUDkeljdVWVs0mb3sRBGQCPQb3KfFuYv1wu+uwVqDty+GjrisPcgNADTiZNVhO5Mwvp71kJHw+nfh8OH4Yc/BNOEz30OkkmoVr3fdd07juPMPvZ0SfHPk06rrFxpsmVLFSGgqUll0aKXkch1CkEDTvUVLUuJBcTONJGXwNFhODIE3c1wbBj29MGli1+VQ/v4+MygEaCRRed7Ghc0QoiwlLI4y1P/NNv+vsDyAcCmxgQDKKTIKha2q7F77mLqD07St3ApmWScY0GDCiEMarxz+l7a1Ayqq6BKBaslyaN169C6a2iBKlZMZyjfRiKSI0CNvB2iPjzK2OJGsqE4j7mXknKmGdfSJN0MUZmlIoLowmZ/3TzcssatlV/zbbmCpAjRdEqYRQiBJq7mOqPKL+unqAioCElIFpjTOI42tZDxJzq5dh4EVcmavEDm4JFBh8mrFYINLs2KwrALIJCDKhxToQzkNWxVwkEBaTzlUAIdl6FRg6FcA4GMQriqYo2o/D4j+OTbvKq/fA2ihrecdcdvoFCEv/0rr0PPjoMwtwsWtHnO7DUb7t4CB4Zh1Rwv4f35S5ychErFE1H5vCewbn4b/PRXkMnAtZdDYpY2NJMZ+ObP4T3Xw5J5s7/PQgje+94QF19sYFnQ0aESCLw6f7U6SB62C3y3UKZ8JMCHuoL8ecpbinwlhExAeoUDjgvRl5F75uPjc2ZsKoyw/3xP44JECHEZ8D287rEdQohlwKeklLcDSCl/NNu4cxJYQoh5wDeBtJRyiRBiKfAuKeWXX43J+5x/HGxcHMapY1pahCggQpLx5Q1oWASG8xj9gkRgkmWJXbQGxhBmnKJIYuQrXKMf437zBAeizRBzKFsRhqoRynYYFZtUbIxecQgjUCUlJihpQVSg1RpgQXU/uwOLsHWFoFtmtNqIkAqaYqNjsUTozD2wC47sgZ7FsHAlAHOEyV+YMe5qzKG3FNBRiDut6OU4sajDxJBLfb3AsmBsTJJKKGw/ANaEoG6Bg5WzmeozcfsUL/EdIIDXqscAhISw14Q6G1OobjIwlzkI08Ua1FEVgZOBWsWLXLnypE3Bu9bDRAZam+DBDfDUfXDxZ0+2vdk9AFv6oCMFj+2D+U0wZ8bOYcUKL2IVDkP7jDN8Txf8/ee8JcbwaQSGoUNLI0TOIkAURdDdrb+s++RMPCsn+Dr9HH8wQnZbnBNRgyu/pNKuvjKB1ZGG910N24/AmvmwyG8i7ePzqiMRfi/C0/N14AbgHgAp5Q4hxFVnG3SuEazvAv8R+PbMwXcKIe4AfIH1JkEngEGAglNBdV00tTbj6itIZ4aY/8dBAoN5lrceRm+woeZQ7UzQn2xDLUoue3gvj5lP8tUrP8bP1fVMOs2omkumkkTTLBprQwwF2yiJCDE3i3Ad5k1uYU6ln5prsFLuIBOKsdNcRp2VRZGSHfErWK4k+czUNMZDv4RwDI7uhVQjpL1ytXeFhkiXj/B0qY1cdR4hV+fSZsHqL2pse85m5y4XRRe0z1HZOawQtBz0vYLVzRDuNfifW/DsG8qcrDQMAAIMvYIzqeFEJG5WUF2s4EagkpDIKYj3wVQZvvev8MH3QsspSd5rVp78edlSsG1oO6XCj1xjcAAAIABJREFUznW9RXtF8f49NS/KMOCqWT66I1mo1KDb8LyyXoxpwK03QCz6sm6BV4xEEqAETgxVSpAOp6tt3p2DB8cgrMF7m71ekGdi1Xzv4ePj89qgEyDNgvM9jQsWKeWJF0XjT5OocZJzFVghKeWzLzq4/RLm5nOBo6DQyUUct59hUpEzW0CnSmAyQ7CuSo8xiBgvceBXZSZaGxBNCqn3SWSfzfh+h3o5ygePPsC+v72MTUqRmm5iiBoBrUp9YBxDq1KUIWxFJWrl2a5eTC4fZ2llD8nJKm1HS8jL0uxauAYhNFaoF7NeayFu93sKJBKHQhYsL9xUYYxR8TBzQxHaA0eJuRHqlC5MBUBw9TU6AyU4fAL2boOlcyXJhMKglMwf1vn4uxS2dLo8mnFgHC9TOykgIsByqBUNREqiRSooJahlgzg1gVioonUJSjFYPgrFIvzhfvirj83+2ra1vVBcAVzUDgdGvCXCy3qh+ww5WQDP7IffbvLyq+a1wkeve2Gu1WQGvnuXV2V4/RVw7SWnP9ZrxRpRz224/O6dI2Tn6dzYptE2i09FpgZ3DkJKh7Eq3DUIfzPn9Z+vj4/PSSwqDHPgfE/jQuXEzDKhFEIYwOc5hzZ/5yqwJoQQPXh/6yOEuBUYfrkz9bkwaaSLq5yFqPIBDogEGjauABnWCQ7kCbt5chunscwIdm8K2Zdj+lgNa2mS4q+DDOxs51HnatZc/zjaikb2OkuQuqAhOEZYL2MLDVdRcdBRVJe4zDEebGLyaIXFx1IIa5JL7wuzqnstZmIhIdHiTay5E5asgQPbYPFq73fAoQIIdGLYSpGQUuTUQMjB43DgOMxphcETsH2v4PKVKqLgLd0dzsGtzQraDngoKmHMgpDq+QDMRLKUaRcj5yBUF3vKwJ3WkdNgxgSpZVDdCLk8zJs7+2vqurBnH0xOQXcndHZ4200dPnzZC/d1HNi4EYaGvAhW+ymNozcfhoaYN7Uth+Bdq71m0s9zpB+yOe+6Hnvm/AgsDYVbtCbWx9LUVnhadbb8q7I7o5c1r1BzsjbLwXx8fF5XJL7R6Bn4NF4ieyswADwIfPZsg85VYH0W+A6wQAgxCBwD/uLlzdPnQqYx+AFurLost/7Is9NlSqaLCLlEohUCW/NUTYfQWIlp28GS4LgaZqnKls+uIrcpxsFiEyee6mB+2wEWR3ZTjZtYuoGFjoaDig0IqqqBreiEM1l2h+polRl6FNBqjQQn2lGTLScnpShw3S3e4xSCNBGgiQqj6CSI0PWC559PGpfS6084pxUa6yHTDF+pgLIdlgrQ2xQacy5jLYa3lldzwFBRSjaxeJZAXYnJ8SbciOotI45BsRnWpUCbC0t74abrZn89H3wYHtkIwQA8UIOPfeT0YmzPHrjvPojH4dgx+Lu/A23mEzq3Ge55BnYfhaAOP7kP/vo9EJxRlMmEZ1K6rx9WnOdCoIgQpyla9mg0YG4EDhUAAe88Tb9FHx+f1w+dAM346/CzIaWcAD78Usedk8CSUh4FrhdChAFFSpl/qSfyeYMgDHTzAyQPPc66XRtxCjkmSwbJuhQbnyyScCEct2h+7AClBWka81n2JJexu3kJVoPGXnsRA/ta2XtwEYlUjpULNlFvTlARBiURxsRF4CIEqLZFqFrBCWhMTO6htOJiWoYCNJy1K4GHgkEbN2FRQCOM8qLbeV4HrFwIOw5AYwpuuwksAf/51zCWBVSvSm/9PEiYgv0TcDRgkB0DKi4SEBWYOtZIrapDTfGMUwugH4D2XkgshRsvhdAsieVSwlObvMiVqsLEJDy39fQCy7a9ZT/ThELhhXlZ65fDrkNgFWFpN4xMQt8wLOyC0Sn4t42Q12D/FJhlODwKLTEolqEu8cq8rl5tNAU+0gYnymCq0PIa+Wf5+PicOxZVBjl0vqdxQSKE+CpeznkZuB9YBvytlPKnZxp3RoElhPgPp9kOgJTyH1/OZH0ubIQIUJ58G7L8R4xYAcWwCS5K0Hj55Qz9eBPNZYvLF1bRlGGOdITZby7ALBeoVcI4Iw6J+nHCR6tMKi0cOriI1SueQNcVFFxq6LgITFmlZmpklRjJkTH6KwItAMPLhlmTLHCWlKSTc0XFYBbPArzozwfWw3uu8ZLChYCvPw5jOVCSM/3/qvDkMGhtkqW31egaVtj/hE5mSmFkp87UdAP8/+y9d5gc5Zmvfb9V1TlOzkEjaaRRlpCEQBJZYAwYsMEB7GXx4uzjvGe9e85+l/ecXfva9e6eTd/ntdf5GDBgTLQxSSIIlFDOcXKe6ZnpHKrq/f54B2tQmpGwJQF1X1dfHaqr56mWuvtXz/O8v6cICKBy6B7ABWYI7H647w41MxCUIGpthQMHoLwc5sxRwmY4BuVlkMmqWYSnY948aG+Hzk746EeV59Xvj0WHJdOhtx/aeyGRAe/49td2K3FWUwG7uqF/GNr64KHHlGv80rnwwTVTfEPPE4YG0xyzUAeHiwqnRHharpdS/nchxO2oEuGdwDrg3AUW8OZ6pFnAMsaXKAK3AK+ce6wOFzulC29i984u4kc3UbGglGDz15hxQyv5zh/Q+eJvScSSrG4pkM2NMpLy4YtlGBtzEUomsb0u3L4spYPtIL1U7elidGEFLq2AjomJgT+VpujYKD2BcvYsmYvecgVD4QDL04cxSp/lSqbjJfq2jqE3pnyTaieMpenLgUhDIAXmMGQEjASgLgLHRnVKEwLdLYiUQbpCEB8Fhmy1XsSPGhToBrrg2RiE3fCVW+BHP4Lf/Ab6+iAYVKv9KqqgeTbs3qcc2VetgCtOORJU4XbD7beffvv86fCTZ2AgBpUlyiV+Wg143cpXq7oIZpTD1bNBpiCbg/pK2LYP3rcK/L7Tv7aDg8N7GxdeqjmNgZ7Dm6e77wcelFLGpuLvd0aBJaX8GwAhxHPAkjdLg0KIbwGPvJ1oHS5uXH4/Sz77VWzTRBtvBCpbUkRo2nTGFq7g2K5djDwyQH7jENXzdtA9bQ4lI33kzGISOR+ljz+HNxln1sJBlje0c+joInrLa3H7cgiXhD6bI+4mjjTPxmdn8Mg8A4VyHhtsoi/9Bo3+dcwSZ1AbU+DVvZAtwJ9M6I+6fjZ8fz+ke8HaBFKDuA8y5RoLGgQRoLgCNrfCjBrYPQSFUQ0SNowBveAKaRRaJYcTkh8eFtz/Q8HAQcjkJQUkPrckYAjG4oIrrhRMmw5D/VAoqKb3c6V3GBoq4MpFqiH+9d3wvkuhqgz2dcFr++ADl8GfXgPHumBwFLoHIVQEP3gVKsKQH1Ou6M21cMeVSpxdLORteHwQ9qehxQ+3ljG+IvQ42Rwc6VbZyBk1yprCwcHh7ZMnRxdHL3QYFytPCSEOoEqEnxdClAHZyXaaapN7PcetGBm/3Xi2ETq883hTXAF4i4qYe999xPv66N27F6usnlxHB2WffZjcLSvJ1dayxNXP8AbwjHUxrXmIyvYD+PdrrCgfJGZNZ6CsnF5PFcfCjYxVFJO2/IgUSF1gh0E06XSPVLOx8GvKI6spEqUnxZQnR5oUEaKIM6S0b11xsqC5uga+dQV8bzvE+qE4CT5NleCK2wStEgYzUOKFcgPGyqGnEmSnhjkEpg3YkkKXCR7JQJfA0gwsLyBsEJJ0TpArSJKW5KkOjbQuyKagMgfTnoO77zy3f4ugDyypzEZHk1AaVZmyJzbC6kXK6HQ0pbJZoykQHkiZsGcYQgnYchTySbhmFuxrg/XFcN0l5xbLH4NNY7AtAfVe2J5UzfBXFR3fvj8h+dKP4dhRKLXhjssFX75TGay+iWnBa3ugYwCaqmBFi+p/c3BwmBz7TKtT3sNIKb8phPh7IC6ltIQQKeDWyfabqsD6v8BmIcRjqE6U24Gfn3O0Du9YIk1NzLn3Xtq2bAGvl0hJBE/vbty/eB63R419aV09i4w+SJFVINVp4n/DpLQsjT03TNobIOBJMEvbT5E9ym/sm2nVm9CDXkzDRdCfZCRQwt7YNEIbvsnNl/8f3LzVOXMXWxllmNnMp/YMOt9zCrNyTcBXFsKn50DmTgi6ob8Xvvdz2PYq1DbBiAZ1ZdDVCkPdqpdLlqqGdyMO8qiEvMQf1DCzNpZXQhVQiVJAObAOCzImbH7NUr/wIcHRYmj7T/AXwa3XQDoLm/ZBNAiLm0/diJ5MwW9fVNc3XAU3LIP1u6AkAndeDbmCElw+j+opiyXUY7uOqvJhrB962mDtPhgVYAShqQBuL6zPgzcDK7xgXATfqwkLvJqKxScgPsFpr9OU/PVu2NIOejHE0/DQy5LbVgtmTrCzeHYLvLoHioKwpw0yuYtLRDo4XKy48FDLaVbgvMcRQvzJhNsTN51RB011FeHfCSGeAVaPP3SvlHL72Qbp8O6gbuVKrvjWt+hYv57s2Bhjw4NUlPZRWgWHjkCpnmesIoohhljYYlFZCWN9Ams4i+2FytQAjXobpq1TN9rFv4S/xH45F0YlGKC7xzgSnkHL0Kt08TxNfPAtf9+FC4HAYPJxLwMDauRM4ISGar9LXQB+8Qz8/HVIZ2DvUfjTD8HMBnhoF1SGIZ6DeD/UVEFDOXQVBD0Z0NI2pimgSsB0G/pssIWq1s+TsBnoEBC1ISeQmqCrRPAfj4LfDbuOmBxstfGHdPwf1mlpPDn+tethx141HufBx+Ebn4NrJggGKWHRdNh2FBi/HQ3C9BrY0wpdw+AWsOOIyu6Uh2GtC8qroL4aHk1aCDRW+i68wloSgjcS0JEFl4ClExYEHBsXkiIHIRsyQRjpOzk7tfMY1JaB21Dlz53HHIHl4DAV8uTpoPVCh3GxsmzCbS9wLbCNP4TAEkLUA0PAYxMfk1J2nH2cDu90hKYx50MfYvqaNeSTSczs/+Slz15HtLiNVDlYBwdIrJiDkTVxJ3LsWpcnngB5dIBBfy1lywIItyRf8NAjq7ALBkbOIh33Eyt1oWNiWCa76q6iOfsM1d5VeDluljSPJeTI4mfyZWjr1kFjI1x66am39wzDwxshUAT+MMSTEPXD0plweCbMl3B0AKQFn7sDZlbDD34sSMRd7NsjydqCfeWCkbwAKVR+NyuVG7wANAl+ATmprHldgs15+NJfFuhoy4Kdprpa5/bVUVoaT/44WuMJMJehVgqe9G8h4EMrVbxSKgEoBFy5GPxeOPgUHIqp1/F7lIjsaweKINtrY9cnmSlh5WlWYp5PKj3w5ToYzEOpC6IT9HORBtXToKgKhvvVW33zfNWTNpGyCPSNQEWRKqM2VeHg4DBFLGcV4SmRUv63ifeFEBFUZe+MTLVE+Bv4/VgxHzANOAjMPYsYHd5luMICO7wLjTGufuAn7PnBOrTEo2RGcoSe6yN5jZd8IMDosE2kUWAWGaQ3DHFk9Rx022ZG6gjrwleTjfvIJrxofolb5ElLP4FCmr2phVxGH9M4SvUEgaWjT0lcAdx661vtDk5kJA2hAGQz0HMAMiPwlA53365KdkU+WBpUwmRRsxItAT94XIKrrxXkcjC3Dn61UyOWQtmU60KtNszbbx02aAE5icsv6OgoYOUkHrfGSG+eQ7vSXHlpCO2E0TLXrIKRuBqBc9v7Tn0MmgbTKk9+LFIMM+tVo7udh6wJtgliFKIjEBmC0oDBDVVT/1K1LCXg/li+WhFDXU6kOg9zCmB+DBI9cIML7lkuTorjg6vhFy9Aez9UlcAtl/1x4nRweLfhxkM9zsyqKZIGZk72pKmWCOdPvC+EWAJ85tzicng3IDFJ8SA2w2j4EKUHWf5XHyHbdR/tr7xC38v/H10Pb6VrGhRMm3xMo788QgEXvbkKusxajgWnM5KKcNnaTdzc9zvi/ghrb1vNQFUZ1rBOaWU/lh5hlBTVk4d0SryTmFiWhWHBPHj8V5DqB+GDN7bDQ0/CJ++AF15X+ujam6C0WO3zqXvhpVdURmn1SvAUga8eBkc0Wg8KOochcyDHcBalRPKoT5oEdCjTIBMpMBTP4LItNF3y6ydMdu9L8en7fCyYf7zuFQnDJz869eO1bVUWzBXgyAAUBeCOlfCbV6E3CQkB1WWqzDY9pnF7c4CKNLywS/l0LZylSoenoqcPfnS/co2/7+7J39s/FOkc/NdaQTwDVYbkO9cLSk/jJ1YShi/dro7f43rrvEYHB4fTkydPO20XOoyLEiHEUxxPMulAC/DwZPtNNYP1FqSU24QQyyZ/psO7DYs8Ah3JKDZDGNQAIJEkOciB2us5fNf7KbrjGi55+t95+q5vIy2wXDZ+LYvnwwF8/7meZMqPeaXFpYk+etLV9NVWENCTfPClJ3n15uU0VBxj1tA+3IkqClEvppZBw43GH3ZJWFEAblgOz7wILrdqHo/HoLUbGmvhvg+fvE9ZGdz5oeP3N7eBkVA9QkuXChbuAh2D/3olQd72Qb8OQcAHy2oE1y+Gj/yNjy9+Ic3AAEjdS8Fws/8YfOOvM/zyp0GKz8ECzLTgwfWwrxN0DQaTMJwBIwd15XDJHBi24dp5YKdgRgUsqIP/+hUMjagy5Mad8JkPn1pkdfXA6Kgabj0yClWVJz/nj8FYGpJZNRC7bUgwnOT3AsuyIJFSmUX3uGWDEBeX/YSDwzsBiVMiPAP/OOG2CbRLKbsm22mqPVgTHd01YAkweFbhObzjGWA7g+xCx0s9KxAIJDkEHixSPEk1/WSISI22/BgvrlzAvH/9MH1f+RUyY5PeECNc4qFsejGhxgL6i2NUNPmIV4TJGD50zaJ2uJNr9sRI1hvMf+BVvAuaGJyj8/PynVR391Eea2BOy014y95+c41lw/2b4EAfzF4CgwOQ6IJwGdwzRSuFeAae3AWNUZihQXsMbrwe1j1tsGZNkM2bMiSTgrDm4t4/8zBvLlx9KVSXu3nxxUoOHza574tZKksFlg29fZJDbbBi0dkfzxtHYV8HNI73YVlZWLcHaqoBGwba4bv3wKUTEtsdPTAwDA3jgqpvEPYcObXAmjcbei5TWbWK8zg/sDwCs6rgYC/UFEH9uHPHWAJ+/phyrve64eO3KlHs4OBw9rjx0MC0Cx3GRYmU8mUhRAXHm92nNFNoqhmsievkTVRP1qNTD8/hnU6eBAPsxE8ZeZIMcJBqbiHL00hsBphJD9WEZRf78i76bQPT58N7x/Xc6s1x7B/WoYk4Vnscc18cw2tgm5KGEUmkZYR4WZR00EdZYgizvwS938ZjFUh1xzD0bbiHYuyKNrG+s5FPHXuEW277CEQrJg/8DPTH4XA/NJVBY4nKXtV74Z5roLlxaq+RM9WZn3v8kyQElJXD174E+w+66O52UVoG0RJ48Lew76gyAf38x6AkKmhuNmhoMujrNvF6oGm2F48L4nG18vFsPJx6YxD0Hi+L2QUoBVZUK6uJ5AhUndC65nGPn7mON9PnChA4TenP74fb3j+1WAoF6B1UmaU3S6vniq7Bx1cpMRv0Ks8ygOfWw/Ao1FepLNYDT8E3P3NxzV10cHinkCdHK+0XOoyLEiHEh4HvAi+hukb+XQjx51LKX51pv6kKrH1Syrc4twsh7sRxc3/PINARCCwK2BTQcOFmHi6mI8nTh4cU+8jZA4whSPqqsXHT6RNsuWUhxUYFdemdRLra6dk9ysARi/rRLAUJ5d1JglU+ZLWfpN8DvxvGyBboqncxXc+hDfZSbg3Q0HSU3BIJnV2wthUu/ySUzz3nX1RDU+LCluolairgQ0uhuWHqr1ESgJllsL9PeWyVBqGhRNkwLF8KLFXPe+gZCPqhtEiVHzt6YDQDrT0SaeZpP2YyZ7GPVSsM1j4Hj45AWSn86T0QmeICv9IQpHL8fo6jy63SzeVe1ZuleSB6Qu9SRSlcuRRefkN9a1SXwbL5vC1ME372GLR2AgI+djPMe5sTODQNoieIw9iYek9BXcdG1d92O+VBB4ezRiKQTonwdPwPYJmUcgBg3Mn9BeAPIrD+kpPF1Kkec3iX4sJPLavpZys+yqgcz5QKfBSkjyJpgQamFFgk8NsH6NMihLHZEyvlkmgPL1S+j+vrn2N5zW5yySA9o9UYe/rxBcEdDDMmasi8PEZ5qJRoeYbavm4yNaXYdT40y6Ls6CAryl8iXlmhErQ7fgFNV0Hzjed0TGUhuHw6vH5UCRCPDlGPyjDtOghNddAyA366AbYfg8ZK+PRqiE7I8Gga3LYYgscgZMDKeiWuTqSmHLbvG9eCEmJpeGgLbHoxw+vPm7izFvrWJPoNHhJxnfo66OqGl16GWz9w5uMwTdiwSTm415VC64ASe7jhsx+Eo60QS8Jli1Qm60RuWAWLW9RKyc5BeHkHzJ8Jr7fDFTOgMwt5Cy6vmlrTeN+gElcNNcr24pUtb19gnYpFLfDr36net0RS/Q1HXDk4nBse3DRyFmeX7y20N8XVOMMwuRo9o8ASQtyIGm5YI4T4twmbwqhSocN7iAhNRE5YxttVgJ+MQkHqLIs2sU0bwU0GU8+zXOwnn3exN72AhdoePDJLR7CehlCcqqxGSQSy8+eRS4QxZALDnk+Rv52S629Exg9hj/6UuNTRhEToNpmwh3zKjVvE6YoGqY02Qtt6qLsMfBO6wqUNYvIzMSHgpgWwpAF+9Tp09MF/PA32GJQE4dXt0JqE9S9BLgE+H+z/IPzka299nV+2QVtKZcQW2qqf/UQuW6R6vjp64P1XQE8KfC7IjkkQINwa8bgkmZS/FwkeN2Qyk/+7HDgITzytbn/0w3DNfFXqq4xCeRTW7YBnNsHOdrDWw93XKdeIiWKpvATW74SnXler77YdAa0M5lTBo60q9rklSoBORsCv3v7ROIwl/3h9UcsXqMb8Q8egvBRWOYaiDg7njARsJ4N1On4nhHgWeHD8/keA306202QZrB7gDeADwNYJjyeAr55DkA7vMrZn1HQYn4B4Oso3fD1soI0ebR9p6cblNvHUZRkZjnBd8kUi+TFCrn4S06rw5iOEMhH8hQRWMo/rssUMrZ9OITaEK9iI5S2lhDhdnjryuhtfPE1neS1HxEwOFwY4ZtXx7dxhwqmB4wIrtxNSj4D3cvBP3jAkBFRHoZBTJbb2AeUZVV4MWw/DG/shkQBrfK7f4w/BP9wLZeMz8qSEvjSUeSQ7WyVPdEjev1Sjru6tqR5dhyvH2yMPHkzzyjNJdo8GaFjoZu8hiUzrNLXAskt1nvkNdHSqbFdJCax9CVpmQdV4X79tw7rX4EgbrFymerU8bhVLUUS5uE9keEzNMQx4VRP8Pz6mZhXOroHbVqhtALG4MiMti6rB0ldfDr/Kw5Jp0KhBZIrZoaIIfOJWeHkzzGyEG1ZPuss5IQQsmasuDg4Ob48cBY7SeaHDuCiRUv65EOJDwEpUN8UPpJSPTbLbmQWWlHInsFMIcb+U0slYOZxEsxs2ZiAvocUDIa2a5dbveNrSEGhkhZ9GXwc7qxaQHPJieywCvlGywTQ7i6cxt6Of6sFiYvV1HK7cTHftNErXasyPuSj76OfRt/0bFTt2MhyKsGHucjbNWIpbFkiXuYhne/j74Sb+ZmgDRul4DaqwH2QGcrumJLDe5LYV8PQWuGYBZEbhYKvyBfX7oU+q2X1mAdIpkIXj+wkBt9dL/ubXFh3bwK9B1zaLr3xFp6Tk5HpaNmvzwAPDlIQNGrUxSkoDhG8PUx618Qc03tgv+PKXYGAQHnscvvVtSI0VyOVM/uRujXvucTGa1Hj2ZSgrgV8+Ad/4LHzxs0pgVZ9i9d/q+XCsFwbGIGlCkYTaEjjUA798Ff5sjTqOFXPhQDv0xeDGy2CrBTELCMLy0/hOnY5ZTeri4ODwzsHJYJ0eKeWjnOXivslKhA9LKT8MbBdCyBO3SykXnF2IDu82Znnhq4aqF1caIOUV6NYeormXibsj+Kw4o21RUjEfz8y/jpA3xQKxi5Adp9bo4XBLGfHZw/gLGdrFQo5EXez90DJmjZrc/exaGld8lEzbI+SzBttmLqY8P8ys0EHGtDCzwgfIE+HgWIa5ZhoMP/jWgAiA++w6tadXwZfHe52kVP1IP/wNBMOqpz4fU+Nypi9UPligMkkHDlt892/jPPtrH6ap0RYSLFokWLwY5s+HGTOUeNm0yWT9eouZMwWWBR6PoMRnUR+2MPOCmkqdVEatvisuhs5OeG4tdHak6GrLIK0C//OvJY89pvONv4wi8OBxjRvEW8ezW6eiohj+/COwsxUeeQ3C443htSXQ1g+ZvMpclRfD1z+m/LQ8bmgswJ48LDtPhqIODg4XDg9uplM3+RPfgwghPgj8PVCOymCp+RxSnvHUc7IS4ZfHr29+2xE6vGspnfC/SAgXOetzHBgoo7zyNdxmmrZgDcUzx7CFARKGKSFlBGjmICES5IWOxzXMlkIzG60VjMkiykc30KPF6PaUU1LXjP/QIKVDIwRr4+QKbrpDNVDQqAiOMpgNgjY+D0cvgcAtZ30Mtg1DCSU0guPjcG6/AoYTcN0a2NMDlaXw8F8c713asRe+9fcFXl3nxeMBXVc9VG+8ofP887BpE9xyC8ybJ3nySZPSUsGGDTaXXRZl27YxiosNbr0lxPb9sG6jEjUfvw1G8/DgFtjVbxHLaFDhhqEkdj7Pli0G3/22yX/7RiWtXTrllfDcZlg0S9kVxFKQ1MHjgyoXeIUSYZqmvKLkhNOkgqma4V0TrCB0/bg1RI1LXS5mUinVp1ZS4ri2Ozi8HbLkOUz3hQ7jYuUfgFuklPvPZqfJSoS94zc/L6X8i4nbhBB/D/zFyXs5vNcJud2MHP0oMV8xZngLRAtowsbERUr4yeKljEFMXHjJkZVe7s9/jE3mCrA1MjkvL9vLmZPfQVkuT1EgTG9Eo99TxmgwjO4uYFoufFYGlztPurGGdq2VOqajoSORCKb+a2vb8NBG2NOp/Kw+eRXUlUB9BXzlDugZUs3U06vfugov6AfbstGQGC4dr88mPibweGD2bIFtw7ZtsHixmoeYSEiEgGUnTWyRAAAgAElEQVTLAnzgA8et5a5bBZdfov6GywX/chCeScFYSEC/rRw1LRu1aMXi6FEL8mn0SIjHX4fBMTB1aF4EVjV0ZSFYpoRiyV6YbsMn74Tycki5YWsHVAaUh9ety1UPXfuAWv1YfQ4O8heKjk74yc8hl4eli+H2W89NZB0cgrIAFPv+8DE6OLxzEE6J8PT0n624gqnbNKzhZDF14ykec3DA64KVNRp7DzUgL3mZlNfCxIVAYmMwRAllDDBsRaixBzCw2WouJSBT5HNe8of8DAs/98+4i6vsdTQMHGNwxUx2lyzEn0uwUNtBtTWAy2UhXeUU+Uto4wAWWbx0k6YLN0VUcA0eJne5HE7Cng7lED6YgE1HlMACKA6ry6long7/z9cEfzWUYsd2H9msQSgkaWmBdFo1xy9fDoGA4N573WzfrkqEVROGK9u2Kgv6J/y4awK8PolugIVQNUBNgKUEVj4veH5tjkJFkJwlsDUYlDDcC4EiwILBVjVzOpODWh06n4JVN4I+DawhWF0L08qhugR+8IoyXZUSPrAQVkw/93/788mGTcp0tLIOtm6Ha66C6FkKRMuGJw7C8hq4qvGPEKSDwzsEDy5m4IxCmMh4aRDgDSHEQ8DjQO7N7VLKX59p/8l6sD4HfB5oEkLsmrApBLx2ThE7vCdY0wxjuRY6dt1BbvFTNOTayOlerILBoLuEDqOWmwrPorlsvIUcxcQ4YM3GThugCWpC3cQHIrxUu5r04gaqfV4Wa130JYIcNefjy5jU+grMd1VycMjPcEFjo7YDTyrKsooKqkNj9PAMDXwEbZLziIBHlc/6x9Rg4fKzaOhevszHP/+T5LHHssRiFmvWeJk+XWfTJlVqSybhlVcsQiGL5mbBrFnH63H9A/CzByGegNWXwQ3XqsdvDttsaM/T5dboKXJBwgOZBKrsr2FZsHkzROdb4DMQrjxS6IxENUaHJabbBgt8QUHerTFmCiw3WAkICVjcBEublOXCvh7oG4PGUmXt8Pz+d47AqiiHbdtVqTMQUDYaZ4uuweeWKssMB4f3MjkKHKJ38ie+t5jYb5IGrp9wXwLnLrCAB4BngO8A35zweEJKGTuLIB3eY7gNuGsx9MUvoXdkMfnYi5Tv+Q790TzbVl7BpbmtGMKFQR7d0Lk6s56j5nSSwoMQNvmkB8t0EQmOofnjDFCgwvDy/mgtu608oxGNdhGmzzyAnQ+wJxdiUC4hnfIT3J3jy3M3sSaSwSKNxpkVk98Df3a1ylyVhWHFjLM71gUL/CxY4H/LY7t2wcgIxGI2//RPeTIFk2iJzT0fM/jMp/1omuCFl1V5q6YKXn4NFi+A8jJ49dkCc7w2c66GHb2CjZsDFLolmZEslmVQV+emvtpNKlPAWHKYaG0XRX7B1l3LyAoDxjTQNDJhTdXMXLDXLUj2Q8SlSpHfPQjzI7DAM94ob0MqD+F3UEP7qstVyTY2ApcuBc8UPLpORegc93NweDchEdhn0VrxXkBKee9UnieE+Esp5XdOfHyyHqwxYAz42PiLlANeICiECEopO84+ZIf3EpVhqAxrUL2GwvRmLNfTXG8IMrkaAkkDzYqS1QvcOKjxSDZD69AM9JxJMDhGUdMQNRXdpKSXgBCYFOiVKTKiGCHyBHM+euMV7C80Enfb2C4Njz+LO5xmcz7HTLqYwdTSGtVFcPuyyZ83FWwbdu+GxkbYurXA0IigoHvIjdn85GGLK1abtLQYuN2CQkGVCIU43t8Vi9mEwwKfD24oljQagsbGKM89Z2FZEtuWuIoEiSvShJs76ekJ4qtLU2IP0t1XDcd06AWSAgoWLNWQAUHbkKB8tmB/Fm4vg52jUFYBVzbD+iMQ9cOHl57+mOJJNVvwYnFLNwwlshwcHN4+Xlw0cwqfF4epcCcqEfUWptSDJYS4BfhnoBoYABqA/YBj8ecwZVy+Bsq4lj5+C+469MASyPdjuNwUhz7LnGMF+ouSxA2NMV+I4up+MsJLeixErpBHevL0pJKkDY1GVznLYj/mYDZKh1bFcCSCUTAxNBsrpzFSVESSLrIM4j/PXxqapqwWRkZU+5QtNAzdRhSydLYW+I//yLJwoYubbg4yNqYxMAS33QzhEIyOwZw5OuvWFaiq0mjthN6YQcsSNy3zLTyGJBiGtSNwbGcOrwFasUQEJS4rB8MaHBDQhurbMjTYaUOjgG6J3iAYCEBfVpUGfzEG/7QMrj/DSMdCAe5/Eg63QSgIf3aH8uBycHB495ClwAH6LnQY71ROmfqbapP73wIrgBeklIuFEFczntVycDgbIswmxDT6xRPkvYPgLaaIVYR8zSzrs3huezeB4hRVzZ34yGOnDLx2htGBCB6XC9dQFb3xCCNDfqLFsyhtMZmZ7KVLK6JgurHzWeqCnbS4jjAvfYC89wh+7fyfld11F9x/P/h8BjOacgymbGK9eXRh8vzzkk2bcoDks59Vk5xHx+DffwLDo1Ba5GLlSti9x6J1wEVxVOM7/yAI+XVcmk3NNJvWFAwORejXK6m4vgcNi6Tpgect2Ccha4DbhlIBvQKiNhiC4aSyoNiQVHF6BfxwG3zpUvCeRmC19yjj1Wm10DMAr2+HW687P++jg4PD+UGVCPXJn+hwKk7yCYWpC6yClHJYCKEJITQp5bpxmwYHh7NGw0MFt1NgGIGbeL6Yh4fhxzt04tkQ3owgpKdItYdIGxauUIF9B1t45Ug5YS1HaURSXpFnS1czNxqH+MeWbh70r+BJa4Bi4yDNrqN8QOskapv4M8+Cf9WUZhOeC1IqmwSXAUUThhBWVsLXvw6mqTM05OGHP07xyMMWba02lg+GhiTf+16KT386jKYJNu2A2CjUV0Nbl2DJPDf33At7/hc89zubWMwiGpFEoxq9GR3TLRBpOPDkHFqfnY680qYQH4aN3VAIAgIKESh3qzLhXgEBMGxJrE9SaMtREhAsqjTYP2TQMxuaTpOVcrvUQOmRMcjmVJnQwcHh3YUXg1lUXugw3qm8rQzWqBAiCLwC3C+EGMAZ9uzwNtBw4aGSwRz8Zwdsz5ocFpK05sYu+MikfWiGDR5BIhkhmQ2Tt/0MjfpI5UzclkZtg4dHR2YRKfPy9WCYe/HTZ+4nmN2IhxABsQC3bQJ5VOvgqRlLKDuDytKzP44nNsKWw6qH6oOXw5ITVuAZBlRW6txyk4df/DyJywU+n8A0lSlpPC6JRgWadvwU6E1j0HAAtm4y6e80kRqMWAJ0kwrLTXWdoOsAFPKQ6/HB0QwwhPpIBwEbCnHoDaJdLrG9HthhIrZIYhuSxLoLtCdsdoV1KlZEqIu6ue86qH9rrz6gVlYOJWHrAWieBssXQj4P67dAdx/UVMKqZRdPb5aDg8PZk8VkP/0XOox3Ko+c6sGpCqxbgSxqwPPdQAT4X3+YuBzeyzw/DEfyNnv0AqVNGsktHtK2YPezC2hY3oZdkGSyfkhbuNJZCqaHTI+LLmlRVRrAYwRY253lulkWxRgUayuRYhtYJoIcGHNBnDnl8v1HYTQBX/s4lBZNPfaxlBJXdWXK4uC57ScLrDeZO9fNnDkuXnopx+ioMhxdvtyL16tOfFYshkPHoKMbptXBkvlqHmEiZmIg8XhVc7tAYOclK2ZDfgy2PK6BkKiPsg1YIGKgWWB40CI2eosXO+GC2TqptVloyyv7dlPDjFsM7Eiy4XAxsVJoTML8MrhqHnjGM1cPPQ8LW+CyxXCsGw60wZEjsOcgRMOw/zD0DsBdtzlu6g4O72ScEuGpEUKUAZ8CGpmgm6SUnxy//vap9puSwJJSpibc/dk5R+ngAEhMTHrJWrAvUc3hnE0BgSsIDZfYdHUaDLdVkNgcpnZxO4nOMHbKTbA+QbIXCnEvySHBwIjGjCqL+IjGKBZF6KAVI3yfA3MXEADXJZPGM7Me+oYhcJY+Sh6XKp8l0irLU3kGT1PDEPzrvxbxd3+XoKvLpKLC4POfDzI2JnC5VPP45z6hSnA+r2qOf/5ZMIRBvmBjp21CQZOgS0MPQmU5/PVH4Ju7oK1dko6ZICNAEmQO4QLhiSJsCx0oFIQ6RepPg5kHDLA1yEtEqsDsKsnBnYJOA4b71TzCFdMsDh8x6WoTlMx3AQKXDrEx2HcYGmuVoIqG1f1EUjXqA3R1KR+wM81IPJ/YNgwNKb+sQOBCR+PgcPHhxUUL5Rc6jIuVJ4BXgRcAa6o7TWY0muDUzVtTGnTo4HAikgL9/JjN9gv0Sz9Z3yKO7fso8XIfhbyPAJJAiySTAm8gC4ZGbsAPtsTK6gSqE4z2ebEtME2JlOpHPjRxxINWDu6pd2Hffs25HYvXDZ+4Gp7ZCqURuPXSMz+/rs7gn/85ysiIjc8nePCXGl2Pwty58PG7VVnwTUf3tjY4dgw+c5/OAw+adHZq1NVorFkj+Kv/oRGNQEcH3Hit4PBhjR17NDqOlYLtBTQkGiLgwii2yT2qAxIyWcBWpldWjjeNS+20h/b9Fls2CnwGxMIa+14t8D+2DzLYl8flMzi0J8xl14bx+wQt02D960q06Lq6FhyfYdjRCd//oWp7++JnVD/a+SJfgMdfhf3tML8JPrBSva8PPgj79imvrE9+Emodw2oHh7eQwWQPQxc6jIsV/4njAqfCZD5YoTNtd3A4W+L53Tw7+CStpbORaMRCLq5c+SyefJZuu5bXBlZimR58RTn0jI2umehBCzNvoLltpK1KYroOFSWS4bzkfaUavX0Gr8egMgQLKtW4nvNBUyV84aapP9/vF/j9OrEYdHVCTQ3s36cyVvopsvNlpYIvfM7DwYOSL3xBZ+ZMDVBzDhsa4CtfEgwMCB5b5+UXP3LR3uZGmoDXg52T5Po0yNtgWmhGHtuUoDPhHEzDzMNT306ChFG3YKjKi78wRmYki2bo6PEceU+KW6Ne7rnVQ0kUVi+Hda+rAdW5PFx9uVqdCKqHTMrxyuV5Ztsh2HoQGiph0z6YVgVVYSWuGhqgv18N4XYEloPDyUhnFuHpeFoI8X4p5W/PZqep9mA5OPxB2PvT79Iz00dEG+WoOYsurYa0x09pPsZ8cwfClWJz3/U01HTSta0SLWtROrOP2JFyrJxBrt8NFhTX2ngiJuG0i6JRL/cfhoAbNnbCli741DLlJn8hyWQsUimLaNTAMN76xVVUBJeugG1b4YYbThZXjY3Q3Kx6naQUrFkjaG6GQ4fg4Ychk4FVq9S+tbVwuE8wc7aOrgXoHYS8YREIQCIDMqAhLYk96INsABCgxdENiaUboBmQ8wAG5HMUOvKMGTaGDh63Qc7lY9jw8sI+yR032JSgccOVqkTYPwTlJTB7gvt9Qz189lNqDM1Us1epAvQmoToI/rchjnMFtaLT0NXfzxUgGFQN+IODanRR6TksZnBweLfjxWAOZRc6jIuVLwN/JYTIAQWmWMVzBJbDeWXo6X1Mv9bFyBsBmFWBe1EOsjZCwMzEMbJeg2Pdx5jbOMQVC9bRubuWkfowrooCA3sqCebzzJqvs6rRTyJucN1cjVd7YVrR8Qbr1hE4OATzL+CK4/b2DD/9aReFgk1VlYc//dNaAoHjHzch4LZb1eVUGAZ84hPQ06NuV1WpAdIPPACRCJSUwEsvKSHW0gJzG2zq6y3icUFNjc7BQQO3V0JGEreBvISYD/XdYIOMYLlSIFzKxkHXVY+85lLXeQMTjYThgbQNacGB101+HrX48hd9uFwa+w7AkVaYMU015nsnrCWor5v6e5W34Ps7YSgD5X74wiLVg38uLJqhslidA1BZAvOmQdAP994LGzZAeTmsXHlur+3g8G4mg8Vuhi90GBcl51rNcwSWw3klVDIN789+Q2ZuEzOPPE97TTO+ClidfQVvMMuMXBvatkf41DU7CIheegOlDB2KssF7LYkFddTWJNDyUfJH1nDXCheBADy0F450gs8NCxvBEDCaubDH+cQT/fj9GpGIh7a2DNu2xVm9+vRd8FJKBgdtbFtSVqaj6wLDgPr6489Jp9WqvjebtA0D4nHo7ZV87esm3T3KPf7yy02O9rkoCQm8RRAVNgPtkowHKAiwdbCFanIPAl4dPAJGLMjqIC3VuCTDUMiAaUMe+rfb9C7Q6OqyOdap8cYOqKqAN3aAywW33HBu71WqAENZqAlBTxLSJkTOUWBFgvDFD0I8BZHA8fFD9fVvfS8dHBxOxnZKhKdFCFEEzGSC54+U8pUz7eMILIfzyrzP/He23v000/cdIlXm4erdf8UeawWD4VIkgvft+x0jc+YQ7GhDWJLaaTHqa3PM6nuSVyr/BHfYoNRncd1sid8teeRohu09bhqCGrmkxvr9alVgw1nYLfwxME2JrquUmqYJTNM+7XNtW/L442m2bs0DkhkzXNx9dxC3+62eB9GoymR1dKiSl6apvqJjx5SnlmVqpNKCXbssyouhqhjcQejKayAN2tNS1c+GABfKvqIgCLdoxLttsC2QGhgCgVC9XLZEE16wJOmUYPNmSSgkaO+C0mLVNF5aDJ3dx+O0bDjQoyb1zKo6/Qie3x+XB1ZWw8ZeWF0Dkbc5fNllQEnk7b2Gg8N7DR8G83Dq56dCCHEfqkxYC+xATbbZAJxxiZQjsBzOK6XLV9D4vo+R2v4rKj1ZqkZ6mdn6BPmAH3fewJIeMksE/n6BHdEomHnCLjdlJUFmzriCuEfiJ0wANztJ8GIiQ6DES1fOTZXmZSgFf1IH9VGIxVWz9dn+2HaR5CBj1BNkJuf2S/3+95dy//29xGIFIhEXixef/nWOHjXZvDlHY6OBpgkOHSqwa1eOpUvf6t9lGHDPParUlU7DkiWqx6mjU1AoaAzHJD6vxHDrXHEZLFykLBQ2HINwSOPAfptf/g6srETzCTS3C8vSqCjJURw16RyVanGh7UJKCVoOYUo0TSKFjabZ+HweCgVBSzM8uxYKJoyMwvVXH49z3V54brd6729bCitnnfm9EgJualIXBweHC0Mak13ELnQYFytfBpYBG6WUVwshZgN/M9lOjsByOK8ITWPG//5XRn85h+EN3yPZbeGNAjnwzb6EgfBliPQusjVLCA0cxMhalLiXQM1qcNdQNmEiQQyTsiKbYk3DqMzQnPLiL4Er6+HbP4PD3VASgln18JHrlDP6ZOSweIFuXOi0kaAUL0WcfUpl9uwQX/2qh0TCorzcjc93+ppXJqNWRWqaOja3W5BInHoJXjAIa9Ycv79nD/zspzZFEYtBv6Bhuk55mU5dNay6VNLeniPeK+noMGioN1gwQ7AnbWOOgMwJSgIW9PZjam7qm30MHTZJJvJ4A5JbP2zy0m+TxGJRbNumtFSnUNC4//4MX/2qH7dL8MLaPJnRPH6XwZuZ854RiPhUa1ffqIpTjh+OY0Tq4HCxIpwS4enJSimzQgiEEB4p5QEhxCSnjo7AcrgAGMXFBD7/F2xZvIzc009Q1ZGm4eoVRBbeSNAThMzDxEb3kijSqM9Og9L3Q9UlJ/06zyfIkfoMyUvSaIfCTCuR3HQpfO9xwdMbVD9OMqv8qp58FT7+vqnHKE89u/OsKC52U3wG89E3qa3VcbsFg4MWug6FgmTmzDMvpRseg1gCnn7RZt3LCfJZC5cuGOwOMn+WjlmAv/1bk1Aoh8+lE4+bNJV6+Mj73ASkYHgAkimbmTWjlIRMDvVCNpfn058z2N3qYca8Iq5bbnDnTRG+9/0C7e0adbUG9XUmHWMWmw7ZDHbk+Jf/kyFrCn7xixT/8k95PvGJMNfMhV+sV0asq2bDnkF47ICK+/bZMM9ZqHRGbFv+Xmw7OJwvfOgsYApfWO9NuoQQUeBx4HkhxAjQM9lOjsByuCC8QZK9l7WgzariUsqoLFYOwn5gru+jJL2DuCt9RET1aV+jFBefFFVYcyUjLfBgIcuPbHjKpVFd4SZqCEYSEA3BgXbl1+SZZF6eB53rqeMQo9QRPKfs1dlSXKzzqU+FeOmlLKYpufPOALW1p/5oxuLw5OtwuEvpzR1HJV2am5oyG3M4jxA2Ho8kEBD099v09XkQQkPTLLKxArrHzVVXCrIC2nognILZ00JYG8fo77fwGV7+7X+XkisYPPwA9A97SIVcjBXl6B8bY0O/BVVeekoKHN4iSRtevAFI5dx8/78SfOITYWpL4JvjqyOTefiPHVDqU/E+vA8aL4OgM7fwJEzT5pe/3MOBA0N84APNLF/umHU5nD8yWOxg9EKHcVEipbx9/Oa3hBDrUOMCfzfZfo7AcrggRNDRgGBxCWFK3rLNRxifmNqQAB2BjuDRQpYCUK9puKssesYKWEfdRIPKsKR1AJ57Ay5thvJJ+jir8VPNKaYe/xGprja4667gWx7L5uE3W2BPh1oRd+18+O1G5e1UX6EES+kqwa43oCulE3Qb3H6tTXtK0LoNrlsjePRXNtJlsmW/wbNvuAiHTWpn6HhbBMWlGiU1fmL9cWbODLBggYEQBk//Ls3uNDzxis5ox/honSYd6l0YoSCe9gx7NmYxwz5MCmTyAinEKac950ywJHjHv2lMCVnTEVinYmgozd69g1RUBFi7ttURWA7nFYlwZhGegBAiLKWMCyEmpvZ2j18H4cxNa47AcrggLCBAFW78aAT+AB/qYWlTITQQsKJRsGdUMs+EkB/+35cgXgOv7oJFr8K/fAya3gFL9p/cBDvboKYYUjn4zsNQHYHmCb+7pqWxYJGXjg4Lf9jFliM6B3pA2jZ7jmlgZDi8zwsJ9R6PJiQp0+Sm2QbXrxJ0jvi562Y3I715nn46TVWt4Ed7C+x5TSe0bIyqqxOMHSgivVXASAFzsYFd5KY/CTVSwx/QMHM2br9OyyVhcjm1snA0B/tjSkjNKYG9g4BQ5cHis5z5+F6huNhHbW2Y7u4411zTeKHDcXiP4UdnIdELHcbFxgPAzcBW1NjAibV7CZxxaY4jsBwuGGX84ebZLNYNNpomQSEIVkg+tUBn2ILtrTDaZFJVJ9FNgyMFwcMvwDc/+Qf702+LoWF4ZZNySFi5XPlKgWoK390BdaXK7iDkha5+mHZC/1Jru1o40NSksXYDJDI2hAELDu/LQX4U7GpAgi7AFBTiFgcP5Vmc8hD0CmY0uHijz8TtFvQlJPu3hzHKTPyFHKJX0HjVMQ61t2D2FmBvFjuWJpvW6PQW43brzGrx8KFbNPIFQSqjbLa+vxfieWWhdX0drKhR8U6LquNxOBm3W+czn7mEdLpAOPzHL007OEwkjcV2xi50GBcVUsqbx6+nncv+jsByeFfwPsNFqRAMSMl0oTG3yYAm+L+v2jzeYTISlERSOj5LkMld6GgVmQz86EHI5NRYl32H4cv3QSSsyn8Bj9oW8IJpqcuJMxaDAcjnlcFoNifVQj4LdZ5lZiFvgsdSju0moAFeyUhcMtJm8/WPaHhdahC1acLrr2tYaR3DsHFFcqR7A+RjboSQkNNhzIS+DGR18kaafNDPgZTNhiova66AcBD6spAsQENIXR8chWvPwtn9vYxhaI64crggSByj0RMRQiw503Yp5bYzbXcElsOFJ9cP8R3gmwbB5nN6CUMIVhgnZ8SuatGYsVlj0CPwZAUlo3DtacbTnG+GYpBMQd14dqejG/oHlcACuO1S+Pk6yVBCUDChogiiJ1hNTGtQRp5tbbCvQxmvG7rSWNL2glkAVz94y1RqyWMTDGRZWA+ujJeODji6B1580UXWDjGk5RBegRlzE++I4PKaDO0t5/9n77zD5CivfP1+VdU5Ts7SjKRRRllCIkgIkYMxwcbYYPB6jbHxLsbee23vei/7eL17vXfzrnedFttgbEwyJhiMRZCFEiCQhHLWaHLuns7dVfXdP6qFJDTSaEYzmhGq93n6menqCl/VTFf/+pzz/U6uxQUiZYWl0ibgBt1ESRsYDpXuTpO7b1HRNChyQ8ABDTErgnXNOZCOtbE53/GiMneIvn8fYf45/9MNLAC2YH19nQW8BVxyqo1tgWUzukgDmn4GRhJ6VkPtA+AqHbbd1xTDY5928uQbkE7DVZfDvOnwzX+DLZvgornw1c9DYEidps6MUBCEArF4vjONhHD+/ialZNOaHJufzBGNm1xxtcqdl7nZfECh5pjLoyiWyCovs2YWvr8VjCygg0dxMW5CKdG+OB1dhzCNLOGqcubO0Fi4MIDTK9hzEPZshKoq2NnuwjnBgWxPgctB37agdQAD8EpwmHAojnV/MQENMy0x0wa71nbx4vMePvvZIB4N7p1h1WAFXTCtH1f9WAZePQBhDywbb6cNbWxGmyQm7xIb7WGMKaSUywGEEL8G7pVSbs0/nwn8xUDb2wLLZnSREsw0KG4w4lj9WYaXiRXwrU8fff7l78FvnwJPELbthnQM/u6vh/2wAxIMwF23wgsrrR6Dd3z86AzHV1/N8R//kaa4GEoLBCtf0DGzcbyTArT1CMryza0NE3I5aOmBz12fZXelwfu7XJQVCu64FerrA2QylnqsqoJn34DdDQoun0o8CRMqYbe0ej0LDQ7tSsN77SB9Vs2WSwHpAadKyOlHluRwS4HH7eFwUw5pqIBBX1Tlq1/tY+FCF9OmuQi7YEnFyc99dQO81WylJWqCMMm237GxGXXsFOFJmXpEXAFIKbcJIeYMtJEtsGxGF0WDyrugdw34poD75L5XZ0IiC6sOQmsMVu6GYJEVQeqKwLY9fDD7baSIJWHzXpg6HoQhaW83KS0R1E9Q+NoXT1z/tdeyuFwSkJimIBRSaGvS+eLHdba1Oj7wwXqnxWqhc8f0NO+vjeJ1CD51Y5pbbgnT1SUoL1coLLRumrsOwhUXwYzJ0NUL0ydA/XjYvx+eeQMSiiTZnATdCzishtDZDCgJ0IPMvU7FdJQw1aPS1G7Q2dtNsk/Dkkka8XiSzZuzTJs28IUszrtguFUrymVjYzO6eFGZx+nZ45yH7BRC/A/wGNYN705g50Ab2QLLZvTxTbQep0Amk8jmJkRVNSgFpr8AACAASURBVMI7OI+qjA4/fQ86EtaHubsGDkfAjEE2DhPH92vhNKys2wZPrwKj1+CZH8bJZAzmzJb83Xf9rFhxosIQwqSrS6ejQ0dKSTjsZtIkcArJPdccdXIv3w9FBVAdNdnpEFRWamzfnqOpKYWUAocDvvhFN5pb4X9+C143/K87JQ4HuN0C04QeB8xdAgGvYOdaSTsCKy/owKqKl3hcCqEShYKs5KYbBf/4fQVNdQE5cFgXL6ea/OTxNIsu8jFx/InfhKWU9PWBzweLqgQVAfA6jootGxub0SOByUYSoz2MscrngC9h9SQEWA38YKCNbIFlc05gPP4o5sGDKOPGo9375UFt2xCxIle1+VqgP70afgaIXXDdhfDtr458j7zp42FKNfzD92PEYyaKIti0Ocv/PJxg/nwH4fDxguTqqx28+GISKVWklBQU6ASDClVVlp9VUch61Oc9sSIRFxs3pmht1SkvdwOCkhKF5maTrVt1Lr/cyUWzJG+81MeSJUlMU+FrX/Ny52cD9MRgfN4e4sqP+Xl8Ty+GroLMAAaKFmDFpYIpjixrNub4YbfG+Aku1AIPb76hk02Z4DTRSkvZ2qDzzw+b3H+nwutrrSL+JfNh2WJ44QWD9etNxo1T+PznVcaFTn7RDQPe3QlOB8yebPcwtLE5G5jYb7T+yPch/CHwkpRy9+luZwssm3MCGYkiNA3ZN3ifFlMe/wHtccGNS+Ger8LEQdb+DLVpcU0ZfHIp/IswUFUT1aEgkHR1mej9lJ0tWeLmpptSvPZaGlWF2loXN97oJRhU6eiB0g+NOxxWeeCBQnQd3nxTZ+XKHIGAJJORFBQINA3K3Gke+2mMSATA4EtfijF9ukZ9lYf3D1hx77rJbr71lxob307R1OQhlXJRUaFRXa2zYUOMgmKV7m5JNJLlS/epdAkXW9YqYCjo+2J0Jfr4wY4I+3YUsGKFH59X8PIbEPBJ3npLUlMjOHxY0tUFlafIBu84AE/8wZoRWRCA8SOTObaxscnjRWEB/oFXPA8RQnwM+EfACdTl66++I6X82Km2swWWzTmBdufdmDu2oUybMehtKwNWq5beFITc0JMCnxMqBnkv2XsAnnjeiq7cej3MnDq47QvDsHCxi9UrYxiGwOWGJYsdFBaeqNZcLsF3vlPApz6VI5Uyqa93EA6rPLkS3tkOn70eLqg/fhtFETidcOmlGtGoyb59Jpde6mDuXOttfviwTiIhsKSURNclv/hFH//wTx7W74NIHL52uWDKzUW8/HIfL7/ch8slCYUcFBdrBAIJqqo9bNuls/m9Tja84WT3phpIOSDRC8lua986rHwmg8wVcsPHCggGFA41CS65RPDmmyYTJyqUDNDwOeCzhLBDBZ/t/G5jM+IkkLxNarSHMVZ5CFgErAKQUm4WQtQOtNGICSwhhBsrT+nKH+dpKeVDQggBfBf4BFahxw+klP8xUuOw+WggyspRy8qHtG3QDZ+fB09vh6YoVATgEzPAO4i6K9OEXz9nGXuqCjz1IkyeMLjaLb8f/vOffPzrfwoaDmZZcbmTe//Ug9KPR4FpStrachQXC0pLXR+s43Zax3eewgTf6RTccsuJdV2zZzsIBCQ9PUfSkQYTJwq8bphcBZ1ReHsvrN+jMrnCTWVlkgkTXBiGZNu2JC6XicAg1tNLyK9TWxtCywCGDum4pdtwYL2tYeO6PrKaj5pxbpZdCJcs0li2TOJ20+85H0ttJXz101YBf3gULDRsbM5HjLM0i1AIUQM8CpRjeb78WEr57/mef08AtcAh4JNSyt68bvh34DogCdxzxORTCHE38O38rr8rpXwkv3w+8HPAA7wEPCCllCc7xgBD1qWUUTHI1MVIRrAywOVSyrgQwgGsEUK8DEwDarCmPZpCiOEzPbKxOQmVQfjzJVaKb6j1PLpu2RmoitXaxjQHv49xNYJ//X8+wHfSdZqaMvz1XzeyYUMCv9/B5z5Xwn33FaMoghsuhRWLhhbVmT7dzY9+5OeBByL09JgsWiS5774KNBW+fB28sxdefMcyM13VrCAkbNuWYv/+LMmk5Lbb/PT1ZVAUhQUL/Hi9CqVKkrgIYBXDH42OgUoio9HTC4XlMDk/h8HrPf2LX9yPf5bNyGIJ+zQFBQ48HjvBcT7hQ2HR2WtyrwNfl1K+J4QIAO8KIVYC9wCvSSm/J4T4JvBN4BvAtUB9/nEhVoH5hXmx9BCWCajM7+f5vGD6AXAvsAFLYF0DvJzfZ3/HOBXbhBCfBlQhRD3w58C6gU5yxN5BUkoJxPNPHfmHxKrE/7SU0syv1zFSY7Cx+TBDFVeKArdcB8/8zhJpN14Jbvfwju0IjzzSwZo1SZxOld7eHD/6URfLlweYNs2NopxZyuy228LcdluYXM5A0xSOfCPzuKCyUKLnJJGkwgX1Gs+8kuadd5I4HIJ587wcPiz45CdLmTUrxMsvd3L4cJrK4jiRTo2ejAfrnpkDQHW5qKnWWLZCo7rCSvnZjH1efrmFNWu6KClx8ZWv1ON0nnkjdptzgwQmb5E+K8eSUrYCrfnfY0KInUAVcBNwWX61R7BSct/IL380rys2CCHCQoiK/LorpZQ9AHmRdo0QYhUQlFKuzy9/FPg4lsA62TFOxZ8Bf4UVOPoV8ArwtwOd54h+RRFCqFhdqCcB/yWlfEsIMRG4XQhxM9AJ/LmUcm8/296LpT4ZN87utWEzOBKYHCRHGRolDM+HxOwZMCUfiRkpcWUYko6OHCBwuxWEkOi6SXf3EMJlp8DhOP6amKZk1UvdpHbrzJnv57r5Hl7xq0yY4MLrVfD5rNTB97/fQ2WlA/BRW6tRUeHgiScEmzbnSGSCKCJDaYnKxHo/33nIRcU4jfJi8IzQ9bIZXg4eTOJ2q3R1ZUkmDVtgnUdIxHCmCIuFEBuPef5jKeWP+1sxX8s0F6v1TFlefCGlbD0mw1UFNB6zWVN+2amWN/WznFMc41RMzz+0/OMm4GNYLXNOyogKLCmlAcwRQoSBZ/P28i4gLaVcIIS4BfgpcGk/2/4Y+DHAggUL5EiO0+ajx7PEOUgOHwr3EcI7TDeOkRJWR1BVwUUXBVmzJkUkYkWD5swJMX78yBp1JZMmBw5kqClSSbQlCAV81NQ42L8/SyYj0TSFxsYcbrdg/HgH8bhKKiWorQ3g8fQRCuqUep109/pRHZKHvu1h+TLbFfpc4+Mfr+K119qZOjVIODzC5nA2YwofChcyPDNK/hu6pJQLBlpPCOEHngG+KqXsO0WNU38vyCEsHyq/xGqNsw2rZuy0OCtJdillJB+yuwZLST6Tf+lZLEsiG5thJYnEjSCLZPib75werXTRRieTqcU3iBvXbbcV4fdrvPBCnGDQyW23BampGdm3qt+vctVVQbZsSXHNNQE0TfC//3cZXV1t/OEPSQ4eTFJYqDFnjodUyqS312DmTBft7ZKaGoW9eyWFoQwYKcrLFa64/Pimscmk9XOQHrE2Z5nqai9331032sOwGQUSmKyXmbN2vHxt9jPAL6WUv8kvbhdCVOQjSxXAkRKiJqza7SNUAy355Zd9aPmq/PLqftY/1TFORaeU8oXTPrk8IzmLsATI5cWVB7gC+Afgt8DlWJGrZcCekRqDzfnLzfjZRJo6HARHqb/WNvYQJY4LJ9M5tVP9saiq4Prrw1x/fXgER3eUAwdSrFsXpbDQwb33FuHxWGmhbdtMVq/O0d6eQgjo68tgmpZlxNSpTq6/PsC6dZK9e91MmpRh//4sUgpu/3SYf/6ZNQPwE1dDcRi+/32rdu3BB0feNd/GxmbwSATSPGuzCAXwMLBTSvkvx7z0PHA38L38z+eOWf6VfNPlC4FoXiC9Avy9EOLIlJirgG9JKXuEEDEhxGKs1ONngf8c4Bin4qF8q5zXsOqwADhGGPbLSH4trgAeyddhKcCTUsoXhRBrgF8KIR7EKoL/0xEcg815SgkqV51ipt7ZoJ5ammmjiqFNlN26FWpqIDyCOqurK8vPftaK16uyY0eSWEzn9tvL0HXJ88/rdHen0DSBpqmASU9PjltvDTJlipvVqzNs3JhB01QWLixg5kyDiy/W2NWp4TQhmYKnXoH777AiV1JakwVsbGzGHn4EixmexqA/GniVi4G7gK1CiM35ZX+JJXqeFEJ8HjiMZecE1izA64B9WDYNnwPIC6m/Bd7Jr/edIwXvWBPqfo5l0/By/sEpjnEqPgdMxZqsdyRFKIHREVhSyvexCtc+vDwCXD9Sx7WxGSvUUkktQ7cgN4ZoBTEYenp0pISiIgc+n0pDgzWLSErLT8vhEESjkE6DaSrkcgq//32O4mIHzz2XJBLJ0d5u8id/4uPyy32Ewwp/90PLgR2s8WsafDnf3ehYgSUlvLYKNr8PixbA0otH9lxtbGxOTlxK1pq5s3IsKeUa+q+TAljRz/oSuP8k+/opVkbsw8s3AjP7Wd7d3zEGYLaU8oJBbjNKuRMbmzGMKWFHFNZ1wqH4qde1hMfIjGPOHCgcZCufwVJR4cTrVWhoSNPUlGHOHMvVU9OgsFDi8YTRdTeGUYSUJXg8BTzzjMGrr2bZtSvNrl05TFOyfn0f69dbF+v2ayGZAcOEW6+0jqMoJ0avWtvgtT9ax/r9q9DVPbLnamNjc3IkAtNQhuXxEWSDEGL6YDeyneRsbI5BSvhtE7zdBQ4FdBNuGwfzi45fT9fh6d/B1l1QEIbP3gqlxSfua10vrOm1vqotL4KFZ6es6rQJBDTuu6+KPXuSBAIa06ZZVeg7duh0dBhMn+6mudmDrueQUtLb6+PwYZPnX8xSdIHKNI+DmrBVV7V2bYKrrgowabzgr7448LHdLsu4tTdiOdO7TlGb1ROBtZtAEXDJfAjl3d0Nw/pbuM4ws9Ffj8m2Np32dhOfTzBxosYpZjjZ2Jzz+BFcpAxPgeQJ4aRzn0uAu4UQB7FqsARWYG30bBpsbM41erOwsRtq/YCQJAyDl1oV5hcd/61s937YvAPqaqCjG36/Cj572/H72hiF5zugymUl659uA68KM8ZY65fCQgeLFx8/6++dd3IUFgrSaRPDsKxfVFVimjqtrZI9cQ2lwk0kpDI+m0bqEiEGZ+RaWAh/cifs3gczpkLgJNdF1+Fnz0JfAqQJ+xvhK5+xRNfPnoZoDFZcDMsXD+38398Fz75i/X7z1TBrKuzaleUXv7AicoYBixe7uOkmry2ybD6yxKVkTW605lyPea4Zyka2wLKxOQZDWl9NMiLNIVpJCZ2E6aIdP2UczdcZpiUmpADdCZF+7ktbY1DsAHe+Himkwfb42BNY/ZHLWSm9khIQIothODFNExDoehqt2I3XD719Bp0Jg0xE56qrAgP2GPwwE+qsx7EcarSiW+Wl8PZ2eHsrbNkDF82xIlgNLZDOwIZNEE9CVRm8ugYunA3eQdr4xBPw1EtQkv/TPvUSTKiB555LUVys4vMpmKbknXeyLFnipqzMNt60+Ygi+aim984YKWXDULazBZaNzTEUuWBiwGRlrBuXppLLeZlf0ctGDrOcBXixnEbra6G3Gn6bAsMN82rA0QLXFEBd/kPep0KzCUdiQxkTfCN8/5ISDjdatWGVFSePCg3EvHkaTz2VYeZMjeee60bXj/Rsz+B0SsLdGW6f4UPpNXH1Oamv9zN37pmbFOo6PPwElBXDNSvgN29AgR9au+GdrZblw8Qayxk+HLSEVmeP1YrnVA2wT0Y2Z0WojqQnDcNalk5LAgEF0wQhBNksxGKSsrIzPkUbmzGJTwgu1oZHEjw6LHs597EFlo3NMSgCbq7N0NHVhZENUumLUF+QIYIkQeoDgfWLLpPVNTnSCUk4m6E9rrGt00djGv6kHCZ54bIi2JOAhiSYAgIqLBnhBsYvvgTrNoCigs8Ln79bEghIPB4xqPTW7NkOdu82WLcui8ulkclkESKL0ynQHAqqoRNoznHrrf5hHb+mwZ03WwKqK2ZFEwuCMH8m5LLQEwfRAr9+GerLoMIP3iB87Bpr28FSEII50610r5Qwd4a1bFK9k4d/miadU9m0TyGd8vDvPzf51C0m//p/FYpGePKBjc3ZJi7hzewIT1s+z7AFls15SwyDXgzCqASP6VfoVx1cUBbDj46GhomJicSJFSJp0k2+n4nT6hSML95LkbsDEVV5d+8U6hjHY1PhoRlQ5oL7x8PehJVOnOKD0BCiLKdLZyesfxvGj7PSewcO6nzrr7qpLDeoqNC4664igsHTS3Ht3JmjrS2Ly6UTCgkKChx4PApdvYJsRmfvAcHh5pGpRzrS7zEUB6ci+d1zJl09UFABVy5VcLkEb7wJv9oL82ZAVyvIK4Z2LCHgtmth0Wzr+bhKa1lf0kNljeCJ30n0iAMk5DSdX/zSQEoXP/gXBf/waksbm9FFgjTtFPhwYgssm/OSVnI8RS86EhX4BAVUYuWJHGjMpp5N7EYgkEgmUU0I6xN1bTZHLAcio1Pq7SCSDiCzCiW1e0luruG11YJPlsP0Yih0woUnmZiT1mF3D8RyMCEElWf4gW0Yljg4YodwYH8fhm6yeJGTpqYsr77axy23DBxCe/HFJF/5SoJUClwuEyEMMhnQNA3TEARDCh6vhi8wspbsAa/EnzSpCUNvD2zbLMj2Sa67TuAwoS8F5eXQ1ARtbVBRMbTjKArUVh+/LJsVONwe9FQOMK3QpqGCkuPNNQYrVyncfMMZn6KNzRhCgGFP4hhObIFlc97RwSFeYxsqkiKm0UeQ9SS4laOCoZoyQvhJkMaFgwKCGJg00sohtZ1gOEAq4kNKiaYY4NRRDBeBgKCnG97bZwmskxHPwk+2QmfS+uw2gY9PgkXlQz+vkhKoGwf7D1q2BdGozrw5ltry+RSiUWPAfUgp+da30kQiKl4vZDIqPp+B15vF4TBxZlScbhfTL/Cx7JKRvX0kEhCNSJIxhWQc3B5oaYENG2BiHehZaGy0UoOVQ/dz7ZdrroQ/vAqYhpWnFAAKmCaa07rGNtb/Sy5nmdLanNv4BSx1DU+R6C+HZS/nPrbAsjmv6KSRfWwkS5o0nXSzC8l8VOacsG4AH4F8ux2JZAu7aKadCYqPaleOLp9CQ6KGCrUVqftwtszEkFaxdTZ56nFsbIfuNNTmK+CzBvzuAMwqBvcQ35WqCnd9Bt7bDLEYLJrv583VvTQ0GEgpufHG0ID7MAzo6AStREFRJOlOSS7nZNIkhXvu8VFWruH1O6mpUqmuGto4TxePB7xewaFDktoywZZG8IehrQvKa+DP/wxqiqGujmEvPp9SD/d/AVatUUnGdKuIjhyEJcGgxqwZw3u8c5FUSvLIIyaNjZLFiwU33KDYNhbnMHETVg9w37IZHLbAsjmv6KGJHCZuoii4yGIi2E8tZcDJK5d76aOFDgoJIzTBUiXGhPKDvN4xgfdj88j0hZmSVinJQZFpMrU6h8SBOEmzhKYY+I+px3KqlkVELDt0gQVW5GrJhUeeeZk+TaWjI0d5uZNx4wZO6ZkmFE90sN+pEc9JjENxkskeunsk0T6Fv/52gBWXn506DU0T3H23wpo1Jr2dJnPHC7zFCokEfOYGWDBzcL5bg+Xy5XDlJxysfEWS7M2hFAiKlvn59ELBlctH7rjnCnv2wKFDUFsrWL9esnixFUW1OYcxbYE8nNgCy+a8wombKBG8OJiEkzgGpZSSpAU4eViimwgKqlWTJSSz/e/yx5bpdDl8mFKSSets1qC8x+D2ur1k6g7yPiGmsRBnfubhsUwqgO3dUJh/KZEDrwYFJ656RtTWuqitPT2b82xW8tOfGsyZ5uDwPkm61wAzTkZPAYK33+rjL//SwcUXuygoOFE4ZrNWBMxzjFuDjkQ7acuxE8lkrH14LUN5qqoEP/iBwsMPg5TWfm6/F2ae0GEMWloMmpsNxo/XKC0981SH2w2f/QpULnXiNZ2IIDj98NVLR1bYnSscsQBpawO3W3zwN7M5N/ErsPTMnVYAO0V4BFtg2ZxXVDCZXWwhQi9OcoQI4ieAHGA7FRU+WCtHTiRoMqtIRv3IXoWSwg6SaS/xrBNHtUKRI0yMXg6zm0nMPmF/80phZzfsi1jPNQXunGb9HC0OHpQcPmyybJHGlPE6Tz9lsFuxmscrisA0NQ4dgtZWg4IChb449ERBOGDVm5Inn9SJ9hlMmWTwjQcU2iYm2CZi1OLlKkrQBmh9evgw/PwRy4fqpo/BwgXW8vHjBd/4BvT2QjBoPT5MR4fJD3+YRNfB7c7ywANeQqEzv5hXTICmFHSkQBVwx9TR/RuNJSZMENx5p6CxUTJrloLPZ6vOc5m4AasH6L1qMzhsgWVzXuHGx4VcxzusQkXFg58UCaYy94N1THSitACCEJUoqJRSyE72YWIi0HApGgo5SvR2Jk3eB0ICgj5vmPZYNT0pDb/HQ4K+fsfhVOGeGdAch6QO1X7wjqCFw+kgJRzplBHwK7jd4PMFyebAlAJF8eL2Cpp1wabXYftWeKsLNm4x6VurQzIJeo4NGxw8/jsV53UmVcvdXHthmtnVGao49dfjN9dYBevhMPz+laMCC8Dnsx4no7vbRNehtlalocGgt1cSGrjkbECCLvjyHIhkrAjjaP+NxhozZyr9RhNtzlFsG6xhxRZYNucdhZRyEVfTQgMGBqVUUszRKumDrKeHQ2SII8xCfp+aQJcZZImjFuk+hIaGIqazougtdhgFRLJhJAoyIygLdOJVs6SlE4UYdYw/6TiEgOpRbpvT2wtPPAF79kFHVtDVrbD9oMmMOpg3z0FBgc7mzUESSROlSMV1o5Nv7NeI7oFKCVvjGXzFh+kzCiGjg9QxImlSEUHqJUGq3c9Pdjqpna3xlRtPPZaKCnh/q1WgP2Xq4M5j3DiVkhKFhgaD6mqVysrhCzNpChQPU+rExmas4ldg6Sm+xAwGO0VoYQssm/OSACGmcGIjdCt61USGGH308Lqe4NnsXExp8kYuzRdFIVdqkmxqNzfrB0h6x7Mz7cM0FJwmjHcHCft6KfWUUMIMvNmJtJlQ4rJSTGONZ5+1amgao9DRKlh0sUrCUJg8GT5zg8aGDTmee05nb1bQXu+gYKZKugPaJKzZCexSiVED3hwkc1YTQwAEHFTJdmfJdXj5xtMqXXtM/uI+Bf9JbuLLllrRq1QK5p44qfOU+HyC++/3EolICgoEDscYvNg2NmOYuAGr+w+42wwRW2DZ2ByDgoafEtrZxX4K2EsFJe4mTFPQmS7jjexBLkrt5MJUksOeYq70NBJ0Z4nqXtzODMXOIPOLipknlvFGJ7zaDgio88Jnx4NzjNXvdHZCYSGkDlhFy+mkYPxUgTtoFZpffrmLSy9z8fe7oSMODQno6oaebUADoCuYOCCoQEeMo6ZRApDQJ5GbBMlKycpVgvpa+Myt/Y9FVWHe3P5fOx2cTkFp6dCEVSpl8vvfx+jq0rniigB1dSNrompjMyYZ2CrPZhDYAsvG5kNM4FJAYSMH8Upwmmm6jRKSupcDspIX3FuYp/lJtzbyVp+LBD2UhDqoD8cpCMSpcl5JJAuvdkCVx0ox7UvA9ijMHeFehINl4UJ45RWrCXVDBKaGoacPrrnw6DopA3ImTA5DWwJ6WkA3sBY6AE1ARoLTDSkJqFjFHML6PSMhbWJmBIdbPnDtHFO88Uact99OEg6rPPJID9/8Zilu9xhTwzY2I4hfgaXDVLJgpwgtbIFlY/MhNFxM4FJqidGiSVKil7ThwetM4RRZ1mYXc0D7Nb8yy2irLcMZ0Gk0NHqSKisScYLuw6RlGnB/MONMAbIDTVU8y0SjJpBlwgSYPVvDV6ohnVBfAzPqjq7n1yCgWZJpQQC6gtDngaiKJbDSQJcKqhtrLQNwARoIHXQNJWrS3iHxOExg7EWHEgkTj0cQCCj09Zno+hj7Y9nYjDBxA1ZHR3sUHy1sgWVj0w8KCpPx0k4f7QZ4yeBR0phSEDMLeM/jo62mkGbHOIRLwUuCw746WqLVzJE9uJ0xiv1JNvU5CAgPYU1jyhhqDhyLmfzwh3HicYmmQUNDmnvu8TF58onT5BQBN5TDY42AaQWswgGICqwFGcUK07lVyLisyBYCcIDUAB0zl6XAq/LCszrzZvhYeunpeXOdLZYt83PwYJaWFp2rrvLj99tNb23OQ+xZhMOKLbBsbPpBQeUClqDQzGM5hU7dTYnoRBg6U5RWehQfKVzoLjcB+nBgoGGSC3jZoCoUix1MqjFxxbwoppubfFMIO4fZRfQM2LkzRyQiqauzhEQ0avL665l+BRbAjBB8yQFv9cAuPwSaQbgUpIEVwRKA4rD8JxQsW3pDByROd4pcJsG2TQ4KC1W++mCOv/tbP9de68OUsCUGb0VBN2FuEBaEYJhaop02paUaf/EXJei6xDnWCuVsbM4CfhWW9uMxNxTsFKGFLbBsbE6CFz8L1Sn8qzfNQ+kYvUY5s9UY/5B8id+5BcKhIZCI/Nc+afm806urlNBBiVpNSchBLz3EaQXqTn3AQRKPW61xHEPwZjIMUJSjaTBFsZadinFeqHbD9jJIt0K7Fzp782JExfICcyqAYTVJBlSHjqqkyJmSbFbS2SVJpeH55+NMmeJkm9/B2l4odlm7eLETdiXgnqqjsy6ltGq+HCN8t1IUYTctHkayWdi61eoVWXjyLlQ2Y4S4Dqt7R3sUHy1sgWVjMwDLNTeXufZB9glLTHkcXJiKsyOQoEf2kRMu0rgxTJVxmS7KZQxNpkHsA++laIpKltzABxoEq1bBypWWq/mf/ikUFQ1u+8mTNZxOQXOzzp49Sd5/P0tFhQvDcHDnna6TfiB2xkAXcP0ymFANT70EPVkwEoAQKEikIpAyicOpEApESSZ1kAZCAUUoKORYvx52NupsqHRQ57XSkGDVe+1PWo8yBR7fCO8eBC8wsQhWXAAzxp3BhTtNdLJEaaOAapQBHOht+ufAAfjRj+Daa+HmsWiCBQAAIABJREFUm0d7NDanhZ0iHFZsgWVjcxqI7LMgikF4QD3AdNfHuavzCZqya9jhmch7/gUEjS7GGV343BFaVC8l2QiOzHZynnqKSBDl57iYiZsFAx9wAF5/HSorobkZdu6ESy4Z3PZFRSpf/KKfb/+fLp5/IUk2K9m3L8jbb6fYvVvwX//VfyG6KqyIktMBs6dCcRheXQt7dkBmP0hd4HQJUoakqLCXUEjS2SlJJk2cahaHplBUCLGYpFu3sonKh4JGDgX29sF334C9XRD0gDCsevpH/wifvBjmTxjSZaOxGf7tx9DQCFcthU/d0n/rHQUVBy7EGJzxeK5QWwt33QVTpoz2SGxOB78KS8PDsy87RWhhCywbm9NGfPDT0bebaQkBuTRa+hBam873k3/Gb2SQQDjCnVXPMce1n4BsYz634ORRTNwkeRUnU1E4s4r3yZNh2zbLO6qqamj78PsVXvxdimSiDYgDUXS9nFWrNE420684CLXF0BKBijBUlcMNV0P3IrjtAsilBWUlKn9crfLv/5YlmZSEQpJoVKIoVmTMMEwqKhzMme5kdz+9z3ISGtpgXxdMLIC2vbDvbZO3D0tCboi0KfzX1wTaIOvQDQMe/DYcbIDSUvjFb6CrCx68//gG1WAJrOAx7v5nQlubzurVKYJBhcsu85w39g9uN1x66WiPwuZ0ieuwunu0R/HRwhZYNjang/MmyD4FpgnqJEgepMtfSWtaY0fSydq2S4mZISKygEhPIf/SdR9/Vv9z7gx1EcZPlhpy7EOlBIE1gy5KJ83swomHcczAOUCvvmP55CdhwQIr+lJZObRT2rhJEolEgQSKkkHSjmn24HbPQtf9aCe5O3xyCTy5Hhq6AAEFXnjwY1B1TFpx8uQw113r4/XX48RiJl1dkhdeSGEYEAhoPPRQATPLVdY1QVMaKl2WfG1OgmaCkrCe93VC83ZJb6NJKidIReDJn5g8cJvC9LrBRZdWvwnvvQsOF+gZKKuE7h6rTdDsC4Z2DQdC1yU//3kfuZwkkTAxDMn114+h6aQ2NsdipwiHFVtg2dicDo5ZoNYCKStVqPw/HGaKpKrSlw2SMV2EPT1EkgVIwC/iNOSK2e3qpjPzAuOUyYxzLMJJKQIHGZLsYi0aTmJ0kSPNNE4/z+d0wtRB9uv7MKYp8flVMkmJlBpSKkCamhpBOm3i9/cfaQl54QsroKvPmixYErCK5D9MVZWDu+466qz6hS/otLcbVFVplJRY4ac7K6zC9m1xaOqCjnaY7AVNWqnDrh7Q802oNWlZbWWSkrYumN7PnAHThKZmK1pVVWldpyPs2AHFAehKQkcnFBaBxw3J1JlcxVOTy0liMZPqag1VFXR12VbZNmMTvwpLh2kygp0itLAFlo3N6aIEgXzBTuGNlHT+gvF6N+3odIgSUsKLNMCrJbmu+gWu8a7E2ethbWoya9VdLA2Xs8xnVWjnSCORuPFh4CJO5KyfzuyZCnMXVfLma21k05bXgstVQk0N/OAHPXzpS4UnFVlgpQsHQ0WFRkXF8bccvwafqoDeDHzvMCysBFWBPZ1Q74XuEEQ1qCoXtO63ZhMWjxPMmXxi9Mow4NdPw9YdcPiwJJs2+fqfw5IllpibNxd+8lOIdYFTQCQF0TTU5ftx53JWYbaUMGHCUXG2f3+OvXtzFBQozJvnGlSfQ49H4fLLPbz+egqXS7B8uXdwF83G5iwR12F152iP4qOFLbBsbIaCfyaa4wFmZQ6yN7iHTMZJXyyEUCQTC/cw3/suPqGiq1n83q28m1tMOtLMPF87ISrwEMRHiD66AUkNM876KRQVCX7yH2H+7z/OZ8vmLvp6JXfdVUhxsUpDQ46GhhwzZpwdQ1CXAo58Ab1pgtsJN8+HddvAWSfodCg4PZYlxb/9HygMnbiPg4dg2w4QUrLpXZ14Er78Zwa//qVkyhSNKVNh2ixgF/QlQXXBFddAeZklzh57DHbvBiFg4kS45x7YuTPDL3+ZwOMRpNOSfft0Pv1pH0KcvshascLHokUenE5wnW2DLxubwWAHWIcVW2DZ2AwVVyUeVyU3KktoSr7Ps20JkhW9LC1/h1muMFmzi7gh6UmF6Wjw0icFv4u3cnt9BaqiMY1L6aMTDQcBikflFOrqFH783wVIGeZ73+tG08AwJKYJHs/Zm0HndUC1Ex57E0wJF02Ca5bBgvHw3lQ41CIoD8KyOdbMxf7I6aCoEOmRZLMQCIJpCHbv1pkyRSOTgboJcMnF0NwKkyfCTUutbdvaYN8+K3IFcPAgNDXBunVZnE6Fvj6B3y/Zvj1LNOohHB5chX0gcGbCqqvL4A9/SBOPS+bNc7Bgwdhywrc59/FrsLRkePZlpwgtbIFlY3MGxOPwk58rdPdOZZbazKaVDuI3BPmv9suonVlFyquyfvcCuveX4Han+HoiyEMTe/jN5yTTRSGFDLFCfZgRQnDnnSF+9asoLS06K1Z4qavr38G0NQmbuyxP0UWlEBiG1oKdfdDYCtdNtWqvemKwtx2mV8H1p+lqMa4aQkHIZAQuN8T74MJ5OS64wHLQLyuFBXPg3S3g9cKVlx3dVtOs6JlhWBEs07SiZULA+vUGTqdKNiupq5No2tm1bkgmTR5+OEEmI/F4BE89lcLhEMyePfZ6Otqcu8RzsLp9tEfx0cIWWDY2Z8D23dATgfrxbqLRifxmi4l3/Hgqxnexs2EqCaHQvacYtSRLJusibXgI08A/NWh8vVYya5QiV/1RU+PgG98oxjQlyofNqfK0J+GHO6wZfrqE93vgS9PBfYZ3kljaElbFPut5PAWR5OD24fPBFz8HGzcJLr1IJRQwmFLvoaLCijYpCtx8PaxYCm6X5YJ/hLIyWL7cMnAFy16gshIWLHDz2GNZHA6DVEpSX+85ZV3aQGQykueey7Jnj8GMGRo33OA4rqYrnbbGeWxxfkeHSTwuqamxzsMwYPv2nC2wbIYfO0U4rNgCy8bmDNCNozPoenrBLRQa35nDlPrXEURJxoI4lBy5uEY8GySu++nJFBLNbqdN+pglxo7AOsLJxBXA7qiVwqvJOw0cjkNLEiacYQ+z0qBVd9UeBYdqRZPGncKdPmdAc9Rar9gD7++AoB8umAorLoO8hekJ2wlhRbn648orYdEia5/hfBpy1iyNW28NsHOnjs+n8JnPnFlq7q23dN57z6C8XPDyywYuF1xxhcarr+Z4+mmTt97R6OhSKCxU+MxnBF/6Ani9AsOwLB80TZBISAoKrHM71ACRKBQXQfUQ/dBsjmLmbQr6mxX7UcevwdLS4dmXnSK0sAWWjc0ZMGUivPpHaOuw6n9cKqiJACsfu4qCGQ0EqlqQYWhrqMRIaahBA60uR0lhM3WyBcQQLclHCY8KufyHkCktm4bh6I3sd8MXLoPXtkMmB7cuhOqTTBlv6IFfvgeJbN47ay84uqHQD18JgdsLDU1QUQiVFYMbR+hDxfOaJvjylx20tzsIBiEYPLP0YF+fRNNgwwaVrVtN1q0zqK9P0hFVeGeTl1xSAQldbZLvfhdWrxc8+YjK1Ve7+MMf0ggBFRUql17qYv1b8PxL1v+dNOH2W0fOz2ukeP/9GM8+28m11xaxaFE/MxfOIgcOwOOPWz0Ub7oJ5s0b1eGcdeI5WN022qP4aGELLBubM6C4CL74WVjzlnVj/u9/hPXvQGPcg140lV5Zx/veCLmJKnRLPNMSKNU5ZMhPEXuQMokQ587U/VlFsKkL3o1CDlheClW+E9czTcjqVlTqdCkNwh1LTr1OOgePbgSPA4rzFlsRD2xqh0v88P5B+PufQ0MzhBX4xl1w5ydPfwz94XAIqqvPbB9HWLBA4/XXDfbskWSzOSZMcLD7gIPGiCCnCCjBUo0RyCUk23YKnn8J7rnTzaxZTjIZSXGxgqIIfv+qFbVyOCCZhJdXniiwmpokq1YZaBqsWKFSUjK2Wv/s3p2kvT3Drl2JURdYTz1luc+HQvDss5bPnPfceWsOD3aKcFixBZaNzRlSXga3fcz6XddNViyF1qjCD9+AYFqyTURIezxIh4qjKkcsU0gqKfEGgsC5NRtMU8BVYKXxPAJ2CdidhKnHiKyOKDy6CnqTUF8On7p4cELrVBzotkRWWeDospkzQNfgqgXwwh+hpQnKQhDNwBPPw1XLoXSYZkedKeXlCg8+6KaxMUdrqwYoCDdADrwCMliZzQCQlAiZZsduq0j/SFoQLAFrSj7o3iQUK7V5LH19kocf1vMzQ6GhQefBB60m32OFq64qoqrKxdSp/aj0s0wuB9v3QW8UHIqkqVln4gTLIPZ8wK/B0vLh2ZedIrSwBZaNzRmSy5ns2RNl9ep2mpoSAIRCTpzl42jp8iE9ClrORPVncbpyyGCGetXAKe5CiEE21BtlDqZhVwpm5euYEgb8thu+eczn49MbrOiV5oBXtkJ1EVwxa3iOr5vwIR2BokBhGRQVWS7zwmkZiCoSAl7rg3MsUVMj+PrXFf75n7OUlxu4uhViaUG8zwRdsQSWDg5HjFw6QihUDriP24eiwJXL4XevgCMvoG79+PHH6e6W6DpUVFgCobFREo1CyRgRmwChkMZFFw1Th+EzZPxE+P2b4PNANJPmb74b48ZrNO64Y5jszcc48RysbhntUXy0sAWWjc0ZkEjkeOyxAzQ0xAiHXYwf788v18nu3k8RIapDVZj1LZj1oGsKc/1J7vd/CoX+bRCGg1RKsmpVjq4uyaJFKlOmDM9bPWtas/2O4FGgJ2dFT454b0YT4HHBmj2W3cLO1uETWOMKrOPnDCuKBtbvAqgMwYoV8Poe6OmGSQ5YtsiyZxhrLF6s8Td/4+TNN7PUG5IpMzyseQ+27QWRETjiHfhrdFQVlszvP2+z9GKoLIfuXigrgdrxx79eVCRwOKCz0xJagcCJNWY2R/EF4IoVUFoMv31exzBUDh/Ojvawzi52inBYsQWWjc0QMU3J448foKUlQW1t4LjXfD4Nn08j1JsgzF4+PaEWxfRzUaXKBYEPMjunjZ5Ok0ulcAWDKOrAUa/nn8+yZYtBMAiPPmpw//0KlZVnXo1e6bIc13t18CvQnIV5/qPiCuCiKfDSJihwguaDFTNPb99pA7ZHLRE3NWht/2HCHrhhOjy/HVRhHVc3YfYkeLgD9vRBfAVkItCkwDoXrH7Kqte6aw7MLzt+rKPJlClOisqcFIchm4P9DfDKKti3H5oPB4h0dXPDNV4uWnLyJuCTJsKkk7wWDAo+/3mN1autGqzly9UxlR4cayycC5u3QUsbzJjpYeZEg6uuCAy84UcEvwOWDpMtn50itLAFlo3NEDl4MMaBA3Fqa/0nXSdT4eT9qVEOh/azuChEsagddFqwe+9etv7qV5i5HP7ycmbffTeuwKlv/AcOmFRWCpxOQSxm0tVlDovACmvw+XJ4rhsiOiwMwHUfyqAsnQ4VBdATh3HFUHkaGZasAT89AI1JK0L1ejt8uf54kZUx4I/tcDAD9ROhDPBp4AvBsxEQJuxMQKMuifSB0QDveQUkwZGAJxvhfy2Er8059Vj6EvDieqv5dH0VXLPIisgNN0+/Cu/thoXT4RNXWhYTF0yF3gjkdC/Fhd4ztguoqhLccYd9mz8daqrggXuhsxtKizUKC8ZG6vJsEc/C6sbRHsVHC/udZ2MzRNat68DvP/lbqC3n4FeVLlzBPjLJdp7pjLH2YDHfWhphcjn4qUZwarFl6jrbHn8cVzCI0+ejr6mJg6+9xtSPf/yU282apfLmmzlcLoGmMSRxlTbg3R4IO2DGMZ81NW74yik8l4SAyYP8JtyShuYU1OW1akMCdvXBkrxNmCnhyUOwMwqFLojlIOKEL9XBr9ogZZq8kzRo1HN071VhvQO68xtOFeS8gvbD8O9huKwS5p0kbSgl/Oo1aO2GohC8s9uKLt1++eDO53To6LXsFTp6jl9+nn2ujymKCq3HeYs52gP4aGELLBubIdLYmCAUOvn0uM05H814mJvqwdmXoyjcRLd085t9LdxZ3kURsyll0Um3N8mRyLaRy6bw+6zpPU6/n3QkMuDYrr7aQWmpoLdXMnOmRnHx4AXWyjZ4s9OKKN3ngNoRnOilCksLHanlMqWVijxCJAu7ojDeZ70edEBD3BJcWxI5ViZTtMZdRPoc0KpaOVhNQlrAbgkzBBLLlPPVxpMLrHQWmjqhptQ6Tk0p7Dg8uHPp6EizZ0+cbNakqsrDxIk+NO3E63/HNbDjAMw4t6zQbD6i+B2wdJjsSOwUoYUtsGxsRohOVSH+to/WimrK69qI9wVxZfto6C7FhU6S5pNua2ajNKR+jd69kfIJ75JpDZNULiYdyTLhyisHPLamCRYuPLMievmhnyNJlQfmFcCmXkvYVHth5mlEctLSYIuRoCXmIq6pEJU4J6RwX54ECfo+B+m1HsycA5wKqQT88TDcOxPCH0r7mSbsboX322F9MwQ9UOyG6acZjcvlTB59tIVNmyKEQgJNE+RyJuGwg7vuGkdFxfG1VCUFsGz+aV4gG5sRJp6F1Q2jPYqPFrbAsrEZIjU1PhobExQXu/t9PZ0xoEdwqH0C3fvLSKeceENJrrx9LVliVLD0hG0OE2NzuhGx5bdUHXiRKcktmC4nsclFdGSKqb/hryi/YPZInxoAV5VDkdNKEZ4qemVg0EojGhplVCL6aVHTHzoGHcTJYuASGheV+elTFDImLC8C9zHZ07ATpoSsKNaRFGFBuIG20BbG6wo7O5bQp4Xx1MZwyTRqTpAzHBgTVBzjsmQbBNJ0kEPwVgcsfwH+YQksLraCXX4nvLgZ1u6FCybCtgOQSFkzIieOg1QWPKfw8orH4ZvfbGPjxiherx+vFxYvllRVQW9vlv/5aQMXfb6OfTGVkKmwMCyoKxHnZUsWmzGMnSIcVmyBZWMzRJYsKWXnzr0Un6SdYLWZZaOQ6Bk3MV1BGoKU08eigmuYCDg4vjg+h8kqmul45WlaXG1c7c5Ro7rwdqYIFiTRZwqCF9SO+Hkdwa3CxafhmdRKI7vYCgj+P3tvHibXWd/5ft6z1l7VXd3V+yqptVrW6h3ZxhtgbBPbIZCwxCQQkgyTG8jNcp/MnZvJckMm985wM5MQhxAczBhiEoMDNnjBQtjIsmVb+9otqVu9d1dV115nfe8fp0FeZCGwJFumPs9TTz/11jnnffucqjrf+q06Jml+8k6j5NnJGDYuIKi68FJVJU4/MZnkn2fgo22nCpgqAn6pPwhyf3YGNGEz2LGfiBahPZpnVcsRniluxOi2UE8CviRMFc1xqYgI6lIP95AOZag6MKfBn++CVTKwnv3yGtg+DAOtwVxd6aBlT8iAk9ngtXeuev3/54EHHJ57rkS93k4+H6imhUKVK64FIUye1gRf+4ZPU0zFFR4DisLlzSofugrSPz+Jag3ewsQM2NJ7bo7VcBEGNARWgwY/I4ODcQYHY5w8WaHrNP1iro/WeUyvUo1GcSsGGILWJXBdd+y0HzwFUDyfeSWH4trY0xZ63AEVFLuKJ6NnbR26kCiLgfoKAuUs1jdBgacZoYkISQK32UELXNclFx5hdn4I247xlSn4TwNgLFqyTBX6wvCkDaYm2J8TrM14tIYsehM+LxVsZFkl0ZTDXtARDkRSFSqVKJru4moC6uCYULfg5DzkXFgiYX4+iOv6UY0vTQ0eAB0p+OFRuH7lqRIPM7Pw4DdhPgtDS2DHjhrFYoJSSbBQ0LFRGMnH2O94KIZHoVkj1eXQ/E6dFIIpz2ciq/DFbYLfuQWMt9g3se3BcA5mK0F8XFcC+lOvrIHW4O1F2YJtx9/sVby9eIt9rBs0uHhQFMEHPzjI/fcf48SJoNBoKhX4kSoVl2zW4jPNo8wtW8bWgsGlA5K/vr7EtOLRSgLzVR8/FYVb1AFmLhkgN3uIhdAgxdl5ojEHL96PkdhMiNcvCfEKfD+IGD+Lmllni78YjaW8qopXB10YmCgoNJE+4zEkkt1MkCCM8bL/f86CEyUNU9NRzXHqdjsL6jSfzlbZkFRYqqeRspndRZ0F4bHC2MfMjAeJIyQjnbynuchsZZht9QFcTSURKYGEbDYNKkgbCPsQVnANWKhDyQJVBl+Cs3lojQX9EI1XnTJTh1oBPD8QXa4L9301+NvWCi/uhrEpsGyNYl3DEhqeFCAFC0UV4gqMOWTHVXbrYOcUZAXGo0Ez6nXtcNPrxGKNjcHD34JKGS6/HLa8g/PuVnxuAr4zHGSR6osteFwJTSG4eyUMNJ3f+c8lCwseIyMWa9eG0fWGOjwjkkah0XNMQ2A1aPAGiEZ1PvaxZRw9WuT7359mdLQMBK1y3vvebi65pIloNAg2P8w0zzCGQJAkzA2sQntVmYZWwnyy/8N8t/8+cqSYHB9g1YhPKDFIpOdDiDOVKJUS6jU4sheeeiQQWVffSG1oLW6tRjidRjN/+oJOVRx2M8cwC0gk/SS4lAzJxT6KAoUWzq5cegmLAnVaXyYU5+swWQpu5qqQqOkjhFr3E9VsCorLdzwDzXbJWPO01GqM1zeSc+rEdJ02b5SNhW3kRJrfTKgcLX+aoh+lvBDHq6lY1RBKxsMaN1F0Dxl2wDdRBaAGiYZKHOICJmZhogADr0rTr1iQipyyaJUrML4AtQjsnYdkDGLNIVyzjh9V8R0R+FdDChgSNJ/e5eMsW32IQ3NrmJgZABtG5gRzBfj8Q3DVSoi+qrFwsQj/9CUIhSEchke/A9EobN60uK4K/Mu/gGXBL/0SNJ0D4fODMfjWEeiOg/mqu0PRgi+8BL+2HgYvEpG1fXuFb36zyKc/3crQ0MXV9/NCEzNhS/+5OVbDRRjQEFgNGrxBdF1h1aoUq1alcF0fKYOxV3OceZJEMNHIUqZEnSZe61qM0syd/G7wpBvo9GHyGORy0P7KgB2LOhVvGnP2AcLPPoUcqeOM56itvAzLybH3D7/MjJMmuXoTqf5+1t9zD7H2oORDLZdj4cQJ9EiE9NAQ4jSmkSoO35rYSuLAfpaZCQqXbuCkMcuEvsB7teXE+em6OHv4r5GIBwrQpENcqzETHcEzahi+xFN9qkJDxaG7NkbFiDPfafJp+7+yPXkT6ZSHJ8qozgh6aAm9sTBDlQkORXvIx1I4UkdRfdw5DV+qaL6HGvVwqzq+piDUQJNWFSAG0ydhqgo9TUF1eHPxdMwU4NZ18PhxeHYyeP5UHTI2pCOwNw/JNQa2EcfZpSDRgsbNccCFkF5nSumh1JWkc90UC/ESlR0JZB0KJdhzAI6fhDXLX3le5ubAcQMXJQT1sUZGTgmskRE4eDAwUu7dC1temzPByIjN7t11VBU2bgzT3f36maXZamC56k2cakP0chJm4CL8l/3we1cFjb/f6lxxRYR0WqW///y1pXq7ULZg27E3exVvLxoCq0GDc8jp6h39iFbiDDOLgYqGSuRsxcn2R2HPDwAB77gD1lwBvsfU1Hf4gfYEbmiW/tnDdDvjDL+jn2KoFdcfI3+gjrpBpbxjlGM7Zgg96rP3gQdYddddLL/zDvb94Os4sopSEAxcch3Lb7vtFdPWcPjvE49y6f+6F6E5VGWK7meeRG5ay2R3mBd6prk6thmd09dTmHdh1PEIqT5LdJWQUIhioCDw8FFRqLowWxf4rkrJnENN1NHw8TWHqjSwfJ2YXyRPEukr1E2T+WQrK6MHyakDuG4BV/GpcphWJcJvZ3r427EWJsM9HK8p1KqgGA6qIRE1UDWwVYkiQAccBTJRaIoGDaPHijDqgGpAWkCfChv64EgdRqahMwaHRiGWgck8yDJ0tMMxCb1LTLy8SzYNi7H74Aj0Fpt6NEnpRJJSuoLS5YEhgowtBcYW4OEdrxVY8XjQxNm2wTCgWHplo+bOTkilgtcHBl57/vftq3P//UViMYHvw/PP1/n4x5vo6zu92HhpOoi3Op24+hExA04swLE8DJ3ZG/yWoKlJ47LLGre5s6LhIjznNN55DRpcIC6lhwgGVWyWkME822bPx/dBSydYNThxANZcgb/vW7wk76emq6iuy9HoAE9efy3jVhsr5SGWlg5Q6M8w/+6rMOfyiD/ZRvGpSSqHZqjcO83h0UdJf3IpbiaJIyPkdv2AJc4taPop0fddDmDP7sDu8ojVKphWnuJCCmFUsFJVnpaHmOEkg1zNUtlDSIYJL0ZBP1mEL1YrjBoF8H2WGT5/Em/DnzeplzqYSs2SSRl4juDgdBLHd6gbbbTqDh2pcVBcprx+bGkw6XVhChvTschMDPP95xMs0/JkrhvDDUFZRBDEKKOyNvMEt8wt4Yi/mmUJwV5XUqzX0WQVkQTPjuCrKiEDusMwYgNq8Ot9/RB4BoRKQcxTSYGr18K6Fvj73dC/2Ch5LAclH9wY5ICsC5qAckFBJnTMmsTyCASUASU7ASb4OZW5agaraAQ3MgXwwLPh356DzWvgpg3BHJOT8O/fgulpePFFGFwCV14O11x96m3R0gK/93uBJzh0mkohjz1WIZNRicUC0Z/Nejz1VIVf/dXTC+KD80Gc1U8ipMFI7uIQWOcS14VaDWKxt04/y3NJzIQt56jobcNFGNAQWA0aXCA0VFbyM3RTXXUZPPd48K1++bsAqE+/yNRQlIpj0mxlcRSNWiRMYnIes80lm2lnon8jttCpdcYQf3I7SuVxot8/jB91qOsFXlrdTdFsJm6V0beobFOPcD2rEQiKVHmal5DNIcb6exkVCmv2HcDQHRZiFifMTjQ1SgWX+50cL1o+pp3mRkXj48oj7CorjISuoJwLsanyDKpS4y+aNxHbvgr8Fn6IYMl1E4S1EGVLJd08RVzNEnWLJOpzjDiDEJWE/Co1EUVRJInhY6izWRbqUQ4O19lf7Cb10Zu43v1HhuwpXOES1dv42OY1PHhYMJyDVSmBR5R5y6TuKnS2qLx/NeyxYaQCH9IDC5IvYXkKDgJ9fYEr7EQdMi1RARkiAAAgAElEQVSwYAXGqDEXJl2YCEPIBaUO+TLUgLAKfgmqrgh2VgksAgKwBIz7+BmF2sEQnNQD8VUEVEikIJ2CrXvhxvXBTfyfvhQEs195BWSzQX/Cd90Crw6hM85gBK1WfVKpU+Yo0xTU669fNtb1zi5LUIgg6P3nidlZ+KcvB1bEpYPwKx8487m/GClbsG34zV7F24uGwGrQ4K3Ohuuhd0Xg32oKfERaeiV6+SgxxSahFqlkMixVhtkfXULBi+E1qVTVKCk7R8SrMJdK4a3rJ/XMIXqSNaavW0JJJImzgBvSaVNLHFTGWc0AbUR5lknyOOSbBqhMJUgr85QqMQrrhhjtWoIdhohu8CVrJcfrrbiWAm6ZKeFguD6HlTSGO4XmtpOUBTxbcqxUYFPCo0lT0eeTGJMCa2AOXXdR/QpLlKPMVqMMJ5cSps5G8SKepqO6DnucNZhTOeqxGKLuI3p0osPT2H6KPaEPoIRewJUKg8pHMEUr96yDqRLsmoZsTbBW1VnZAkMtQZbgy0u12h7YftAq5Jtz8MNCIDRiKiyPBDpoBMi7oHogWyAfAvdgsI/uBvWyahogQQ0DdQJxpQCOgEkVxoEMYAIOhAAjCvEotESDQqZCwLFjUKlCf1+wvnQ6EF37D0Dm7HIJAFi3LsQzz1Tp69OREmZnXd773tcvutUeh2M5CP8Ew2rdhUzkzNu83XjsycAV29cDh4/AwUNw6do3e1XnmIaL8JzTEFgNGlwMtHS84qm69n0wOkM9uovpVIayESPjTrPG3I04tkAsbHAksRypq9TUGEWRpKlN59JLfZZuijLxjj5Uz0fxJYomQBNo6FScGqX5IqPJBWp6iLlyG7n+NKq1hBdu3Ug6pOBFJAOu5Emln6MyiaspCCFRpMSuWDziraCVPC6QV2LsNtcSccqUwp2Uy4JkSNDu6iTiMe6OptiVEjw6c5xcKI1or+GpKje7T2JbOvNaC/FKiV1TG9gqP4I/4WPoJVoWTtCbHmVAwJrRYcIyjN53B6Zo//E56ogHj5+EoZ4qzXBbSyCqqj4MhILg+3AMCgYoNiQNaNZgNg5mKxh56GkNanQdcyA0BxUTlBD4NYJYLJ9AVC26IzGgqxs6PKjLIPsvV4C7Ft1/8nTWIRG4An8abr45hmVJXnqpDsB110W58srw625/eVfQJuhMZWI9P9CNq38Kofd2wPdeVh5DvM41usiJhWDL0nNzrIaLMKAhsBo0uAhR9RADSz/Efgbo5t8oEybidyKzVRzbYWX1BVw9xO7QRhaUBJ3uLB/IVBj4zF8hXvoqyw4e48TlA3hCIUEFg2b0epSRL93PifF5pgb6Ofae5STVHKaoM1brQzEEesVjeWgno0aCrOsgFfA9EZTb8iQlM0apLrhUGWaXvwRCVY55rajxDJ+INjEwaLFrfpING7eyvHkb+8tRxtt76WxJk7EniMazHM4tQ5udx4klcJJtPDl6C6VqipqWxDclNT+JX3OY6N1M+WCZj9d3oYo6XS3vhMQbO6+KOFU9/kdIYE1r4B4aL0FTGGwXVq2A8Bw0q7C8DfZkodgGL70AExqUwuC5/DjWijTo0SB4/eY+6E3BQg0QcNe1sHKxivbAQBBTlV8IMgfL5UDYDA4FbrwflYvwFm/6rxcPZBiCu+5KcPvtcYQI+lOeif4ULGmCsQJ0n+Y8+hJGC/DOgSDY/eeJm24IXISjY7BkAFYs/8n7XGyU67Dt8Ju9ircXDYHVoMFFypU0Y3INs0AnR+jXl9Fxyc241RO42ofprb/AtQs70edTpM3rEbfeC3o7svUSrjj8bzB3mL39/cRkPxFtPX17cvjj84T6+3m+dZCOPQdpXuVwstDL9cWnuMLZzgPL389k0qBeVImKMpYwqIswGTFNqzpPSYth2gkmvRYi/gxuNIFnqSREne94daI985QGLVS/nYfMj5JSc6TUEqp0qHoaob0F0v/2KNPOPBllLxPrV2H0V8mTxLdVHHSkrpLrWU3L8glO2g6/Yn6SjFvh1so8d0b301PrBV9iJt6g2lokosIlcTiiwqoWKHtgAZ/MQG0BklFoScL3jsJjh6EvA4/sgOFpKJcAB9QMtHZBR0sgpD55XZHWZB2v2ERM02l/WcD4lITQeyD3HBTHgkzBjTfCn307WMvv3AyTRXj8xcCt+OEbAiva63G2BTYVAR9cA/9rLxxbgKQJcSMQmLka1Fy4sgduOE3G4tudjg74zO9AtQrJ5Pkv9vqmIGn0IjzHNARWgwYXKRqCy2gGbj81qIIaX4nmfBZpPkibaqIM3gnGlSACs4PYfAPtm2/gDnxuxqOGSxiNufJ2hqWkaJjMWpI7+1+kjsFlta2sHz+MEXVYN7WbA80rcQwNKSDl5lmi7aSgJlGEpF+O0t00Tvh4lVn9ZurxECGzBg7IyizTRhQDyIcz2KrBgmyiXx5jSDnK4MxRil89SCIdx4k207zWJqf0Y4cNmkN5VMNnPNeHp0ikVKlWosT7ShRyKUpOgi+fXGD64CO8a9dOtOxSYp2dLL/tNpK9b7zB2i+2wLdzcLgKSR1uT8MTP4BDY4FF6RO3BhlYdQceGAFxFfTlIX8S3AVoiUFrD1RbYX9zjY8cmGdtfJz1LSafaN8ML2sx9K+T4EXAuwo+0AGTZfi//gecOAJ+BXY8AkNr4apNUKnD15+G3/2FYF/LkpRKgZXMNH/6VLeoAfesD8owPD0WFIBVBKxqDVyI3Ym3Zwbd2WCar00yeDsRC8GWoXNzrIaLMKAhsBo0eBui6jeAfsMZtxEoRFCILJaL6Ni4kdl9+5gYHcVb3YNmusQnsvR7k6TcInsSqxnt6qcmI+iGS7OzwBplN2Fpc1z2E5VlWuvzSE1QWxYlVCxg6mH6xQgh06LVn+bZ6tUkrTLlcJKQZ1NVw+T8ZkzfZvDYLvYqKtZshc7VNiMtq6gTIZyyaJJZmsU8tm8wle0BAypWlNxohvbkFJdk9rBEDOPkYPc3D7LEqzLT18Lsg3/PO3/pbiIdKxajz38K3Dmo7QO/QFhp5u6mNdRbI9g4KK7J4ZMGvW0wNgMT89DdCv3dcPw4OD6YBbDGoKxC9igcnoHwdaCNKji04KQjmLHD3PeSzZ1LQ+TLsLIblsfgiVmYrMLXPHhkp8doxaJsqCgFnQNHFU5OwqZLA3HnuMFyJyclX/qST60WVH7/1V9V6Ox8rRoqFiV79/qUy7B8uaC//5XmGE0JSjAMpWF+3sUwBInEuWu51OCtSbkO2w692at4e9EQWA0aNADAiEbZ+Bu/wSWlEt8uwq6RZ7ml9UmqIsKhzn6+n7qKI6Gl1L0QSW+B6NgUK0N7mTV66EhOo6suUldQfIkvBK2RaZJajnYxiycUUqkSG8UL7GQjAh/hS4TqIwSstfeTbW7H8vNUqyp7ll6H2R4lTY6ZSieWG0LXbDpaJ5gq9UDUR00KoqLEqpa9LNGP0r/9EGuf2kVyfpZtlsZD/1sbqUyCp7P/yh8ct6H942TJ4IbCJEQWV51HwyBNN+ZiRX0Hl6Isopa2kqjsREEDxaQgF9htfY0DSgvzfhxHQv3qTrYfv5xEeoCuDkGuLrj7e5KxqoVI2yg5nbJv4leUIMi9AqUpMJpU1JDK3FiMHRMrGC2a3P8gXNoDd2+BOzZCwYJRFdrCYOsWJRSkquB1SjRL4tQFew9Bdzd88Lrg+n396z6qCj09gnxe8q//6vOpT71SGGWzks9/3qVWk2iaYOtWuPZaj85Oga4L+vrgxIk67e0Gu3fXeeKJIvV60KnAtjVSKcHmzSoDA6CqrxRv4+N1Dh2q0NVlsnLlWfbMXNzPMBQymYszsMt1JePjQdxce/tFbN5rZBGecxoCq0GDBj9GUVVCqRT/Z3yY33y+ne/WrqW3dYr5UDuj+hBlJ0ZOppmttxB1EhTU7XQpU5RkHFfV8YVA8X08oZFRZ9CEjyZ8LGmQ9Iuk9RxeWeH7ieuRukBBskQeo9nPUuszGLhDJT+j82w4TWc+T2dmHDscZrIax/dVpCrQO2qEWso0hXMsNY9iaBaJhRzuYITh0BDRLxSJjYwy9Ox2xt91GcPNy/g/Cidp/ebfsrtlOfpyhc6mEkvVEMtDGeql79A24qKqc4z0xxCag6BEON7MNXYS33d40KuRL0UJW/OMWy0MO0sxQhbh5c8x72W5cd965g4p5FRBuN1GehIvJvF1P2iyWF88wXlwXA03oWDNSQptETpqgtkc7K/BR24KXHKtJhxbdM+t7pOMHnGxigqyLlCdIJbrM++HgW4IL7qt8vlTld5jMZiff+31/d73PA4dgnpdWRQEPp/9rMvmzaCqkkqlgK5XaW3VqVY1FhZCPP20xje+4eP7Eikla9dLbn2Pwj0fVTCMQFBkszb/8A8TAFiWz8c+1snQ0GvbQL0ay/L5/OcniUZV/uiP+t7o2/eC43mSL3/Z5+hRAMmddyps2nRxBmjFQrDlHAXvN1yEAQ2B1aBBg9eQViV/1vw4j323yPFkmpwxR+nD65nXMijCA1PD6UqxzbqZe+r3EXPLTIY7Cbs1HE3DwKaOSVJUCH4agy5sdOHx8dy9rJp4jq2xK0h5U9w9tINouEZEUzm2oYe2sQkuN/aSqhaZY4A2Z5qZuTCzqUEOe+tYp7+ILyWuYTAn22lRs1TsKIVIM1Y4jd5SJ7XZpry8heiRE1RWD3AyMkjH849w7Yv/zo5lt/K9d9/ND9tgc3InV0SfZWqpQc2MUHZt5shgCpUoFQ7qedSsj6jbtFhFKiJKa2iGfaGVlLUommNjlOcZP1SlQgws8LMqkVQZt9PGGTMh5wfVSFUFjoBMg+xSiAHtJsymITQPcR+OH4R3rIR3ZGC4BCcrsD4TouVTh9i506R0/yDLLhF8+hOwaskrr9mmTbBtGzQ1SRYW4B3veOXrz+2B//JXkvw8dGYgFpX88IceluWyfDmsX6/z2GOS5mZJaytEo2EeflhQKAi6uwW5nKDiws5hQf5hyfIhyXXXBgIrl3NxXUlfX4iTJ+tMTdlnJbBMU+Hd724mHL44RcncHAwPQ3+/oFKBbdskmza92av62SjXYduBN3sVby8aAqtBgwavQaeV5hafO97VxhPzc9y34hc4qa4EITGEh1CgpDTxhHcTV1vbuay4g+dCmynrMaKqywwtxKhhOHUqWoyQrGH4Dp3VSRInRrh57hj9O/+dmWMOsc/20jNYY7S5l4hhERsy6HZLeHaI8PAMVlmht8kg9q1h1qw4zJPdtxCrlBlLdiNVHfDZH1nDhD0IokR1yfXYlTDpYoVM6wxWxadqxth624eY2LCWwkiaykgKvaxhrdA5EevFm9Npic2wunkvbXKKI2IFFiYhp0Jcq9JsZZmkjWysBUX1USse1aKOVdPxwgpiwg6iv+tgiTCWZ8KkgAUBExJmRVBrwQeiCmyCcjeMz0AiBOpSWAMcGYFsHhaAd6WhNQExTeWQ2g63wdrbTgkRz5OvcNPdcotCMikZG5Ncc43giitOvWbZ8Nn/CaMnFOquR80ThIXH9IQDSL72tRILC2GWLElw/fUJLr88xMMPQ3e3S1OToL1dRVVBlmC24jFy3ONz/91lw/oYiYRCV5dJS4vO6Gjg7lu+/OwrkV599elb91wMRCKgaZJ8HkolWLnyzV7RG6CRRXjOaQisBg0avAadFBF/EJpHaHcVrPY0visoksRQLKRUqVhxfB/+Ov5pbg5vY4V2gJKWYE5mUBwXE4eiTJAsZ9lcfIG0UiBz/AR6u0JuRmKGPOItgvwjBVZ/2KLPOEmTn0PGAN+n5KSoF2JkpvOUJ6JYT8+TPDnOwOVJZpcP4SPIqNNYToSjznI0aVOq9pNPRklZx1mYSTBZGSAWrpOI5Ki6zSwsbyc6M40XC9PfMUKzkSVXaSNkVhlZWEFdCdOUyDJW7WVhoZnaQoie6klWlI5wMtFLuFwl19rMQiWJfTiMqEmqhQjVPckgEbCJ4FtVUcCWQfV2/2VxOSpBI8OXFFiA3Drwu6G7BbYd9bm1z+aIDw+/aKIpgt++ApqTsJbXNv576aUSzc06g4NB8L6mCa65JphrYQH27AFNg6VLYf8hOHIAvLACmqDgSRZ8FV9TUF2Pel1hxw6blStDXHddhHBYYFkOyaSGprEY2wXyZJ1y0ae7FXI5ybe/XeODH4wSiah88pPdTE1ZpNMGyeTPx60lkRB89KMqTz3ls3RpIHIvahoC65zy8/EpaNCgwU9Nc9ev4x34E4bkSZa5w1REjKobokIC6StIJIZZRY1oqJFN3KDdRvPT/43d1lH2be6iLiWx5w7S+60dtPZnWXKLj6N7LExAbkJF+iA0A/9EjbmxOC3lIqF4jdoI7HrYx0xnGer0KO+xOPq1MGUtQvnZeTru+xzivTcy/+v3EO0sM2114BgGmuqRq6Txk4J8qY/QdBl/dYLatILW6VFb3oRwffIDvQymJmmLzzDpdGBrIWKiREjUKWoJpAvFnU1MznehhRyqK8P8YGIL1WyMzsgEinBwDAMGoH7CZOqpbnxPhShBtXaFoIJ7UgRBw1kC64BKUAZdB3Q/GDsMC1PgvhM6r7R5fNKiPFckpxn0izia8vqWoJUro6ctxTAyAvfdFzQnhiAeS+pBD73jc4KQAiCoVcFv0tGLNaIRgW5ofPSjJuFwcMzVqxWee84jlxPEFmPWHdsnovpETIXuboXZ2VN35HBYZXDw56yHDjA4KBgcvPizLGMh2LLq3ByrEYMV0BBYDRo0OC1KywCZ1X9B4sWv8Cs7H2Xqsi5U1afqF/ENhbKIkojYbDSr/IbexRWTC7DHoa+pm5u/cpIXnCZkbhC1SSU09STTz7o47T67/qEOtiSaMXBqEr3DwH6qzqGQgpesM/yIx/ROl1BEYOo+xTkwTQdZLyI1HS2l01yYZ/ZQL3tO9hHvymM1RQl5ZWqVBEQkXlMCr6eG5ttQA89XUZIa1vEEnqFTqsbJVdbSPjRO3m5hvGgQNqpkzAqiLhk/3oNvq9hSI9/bhO1GQZOMFJdixqronS7EwBtRcTwDogLCQIVTPQhdguD20OIJjSyOZwAD6ABiEgagnPM5Mi0gHmP3wxo3b/oeqwd3k4jcCZz+rheNvvam7vvw0ENBMcz4YpugmZmgf14mA81NUCkJbA8UoaLGJQMZQTKms2pNlP7+U7eE9esVnnnG5+RJydycIB4HITSktHBdn3hcsHHjT1n6osFblnINtu19s1fx9uK8CSwhRAjYRpCgrAFfl1L+55e9/jfAPVLKs8/nbdCgwQVFpDsJ3/S/875KhdjJ7/CVhMuE0UTNbCaqu9yghfk1tYM0IYhKMMKE8nXKhRiiFiLV28vwd4/ix1fScSiH48yjhj2EHkaLGaz74FJmJ8tMjU+QytXZ/4BN6aRP2IDOuCSXBUUNwpcScahWHZSmCE21cd7f+SB/536KREuRMoKaiOBroPkunqMjTA+3qKGqPpruoroeTs0gHZ8nLkvUCaO7Dt3JUQzboTM0TpUoXl4lYlawRQgpoTYXxfN1MAQ1EUJRHXTLxVAtaPHxhAoOp8SUSyCybAKXiyRo8JeRoAsiqQpC+rRYc4wm+iGkBM2gpQo2WBGTJx7dgnaHwjXdj4K9CscNmkL/pAri9XqQTdj3soS8aFQiCAxomYygaEC2DGoUejo0bl7TjAL88gdeefBoVPBbv6WxYoXHQw9JJidh2TKD97xHZWDA55JLNC655Cd0hm5wcdEo03BOOZ8WLAt4p5SyLITQgaeFEI9KKZ8VQmwCLt7IxgYNfs4Q0Sg3rbiLa6RkXPr4SDqEQkK87KacbIY7Pwn5WY594xEi0RBSSry6xYLZTWmmC2amsDhA0/IBuq5dz8AvXEmPXSN3YidzBw9T/vo+avYCqRi4FtTdxUa7Gng6RFuh6KiEEwZz5TbMJhu7GiKilGnOTEMKstMZMAT1UhQNh3hmFiklFRHDLykk9BJoID1I6GUSRp612oskZZl63mBr+J0YCQsrZ6I1udSHw8SaqlTKUaJGhXQ6i2q41Ihw0uuFTgETBMIqRSCoHCAJ9BFYtYYIXIOuJKXkKfkJbuh+ki+qvx64C20lEGUE+zpzJi8eWcYn9sSxHZehqGAoovKxOyEeg0oVwqHXCq5QKGitUy5Dre7z8JOSA1OgJWCZkHTGFXRd0NMnuGIjfPhWEL5CMgHt7byGeFxwxx0ad9wBvi/xfdC0hqh6OxILw5ZLzs2xGi7CgPMmsKSUEigvPtUXH1IIoQL/Ffhl4BfO1/wNGjQ494SFYJk4Q7xJSzu0tOMbTyF9HyEEyf5+ciMjaKaJWzeJt1zD6jtvJH2Fhcs4wtDILLmT2qEp9NhfYtgKtp3DMgPNYZqgpoI2MZYDeSXFU8s/zTPHfoVQd42IUcWq6FSrreRzzRAX4IMsufhxhUo2gf+Cju0ZhK0aiiLxBcSMEmGtzBI5jPA8Osvj2EJjMHaMiWg3rmdQHQnRkpijK3GSci3OCX+AqWwHlXCMai2ENxGCHoJvtyJgSTBFYC6KAes4FYNlS7AEs3Yr/d3HGUv2giKhBKQ5lcW1R8H1FUrDcb6fb6We93l6RBCZdvnqF+D3f19nx16FqzbC7Te/8vQrCrzvffC5z8H9D0nKWmCpEr7k4KggucznT+9R2LhZEI8EQfBni6KIt2cPvrOkWg2yBt+ulKuwbfebvYq3F+c1BmtRTL0ALAX+p5RyhxDid4CHpZRT4gxNrYQQnwA+AdB7DnqJNWjQ4MLRsX49w9/9LkY0Stsll2AmEljFIkY0iplKMXj1b6CoKhIfgQIqrLodNLWdF+69l+zhg7j1oxidJmrBIj0kMJuhvHo5W3t+iz3WrdSOR6k/F0ZMSwhLSloCf0BHOD5SV/AdA9VxqGhJqnNx4i0lbl37EIlYgbQ+x4i7lJBqo6gu6WwRKxShnIiwTB4lKmtMVTuxBwxSPTlUAa4qsAoRKqEomAJdtchZYbBEYLmKSFBl4CYcXfxuU2TgOi0KsAJ14iomI4VBhmPLUNpsmFPxx4GaEog0Gyio5J5uC/wAusTTPKw5l11FyV/835Klaw2E67NqUCGREITDgbUJYNkyuOtujy886KN1CzQJ1YqCX4CnZyBckPzjuqCSe4OzY2wc7r0fPnQXrFj2Zq/mPNJwEZ5TzqvAklJ6wDohRAp4SAixBfhF4Lqz2Pde4F6ATZs2yfO5zgYNGpxbOjZuZOL55ymOjxNrb6dpYIBaPk89n2fF7bejqIEVTPBKk8jQrbcSa2/n0De+waFvfh1bGyPaG8VuEwzeorA1eyk5OUhnz1gQHzUVw6ob1PMRvJSKQCJNEEgM1aIuTZQQmEqNSLrIfmM1IbtGmz9NKzPIiMLz4nKK0YMsV4+CAMs1WbXiAJsvfZ5Zp41J2YGieHiaxjrnBSaivayO7eWgWMGC3opf0RazAwXEJGl1FmOJi1ShMNdETYagJgMXogJIkOggJFQ0Quk6IqpQfyqMVyMQZwsisGjVAUUgIgrSB8dWOHFUEDYtBtOCz31O4jgKAwMan/mMgpSChx6Cx58o4/tg1FW8RBivCMID6cPJKfjr/w+W9gTn/MorYM2aC/feeCtj2x6G8VoLbTIBa1dBuvlNWNQFIhaGLWvPzbF+kotQCPFF4L3ArJRyzeJYM/A1oB84AbxfSpkXgSXmc8B7gCrwq1LKFxf3+Sjwx4uH/TMp5X2L4xuBLxGknjwC/I6UUr7eHG/8Pz49FySLUEq5IITYClxPYM0aXrReRYQQw1LKpRdiHQ0aNLgwGNEoGz/+cU5s3crkCy8gXZdEby+r7rqL5qVn/rh3btxI+7p1XPm7v8u/P/Qb1HJHIFFj4vE5/H07MG67CnOwnYVrmnG/Z+BMmWALQqEq3ryBG9UQrk9dhiEiEF02jqFRFlHWyGmu5ocs1UYo2gm2Va+iEokykeghWq0Rr5TQog5ORMX3VSp6mKRXoKgk8OY14gfr5DfFeeHJjRTnkygpG2WpgzttQEXQ0TXB+vALlHNxjJSFN6jz9LPvwFEMkAIUH+pKIK40EJ5EjXncfGWROzaH+Mz/qzJXkEEM18t/VnoKSsxE2FAsSHJZiesqJBI+IyNBxmCxCP/4j/Dtb0MsIdDSFjU9GRwmJJEFCGtg1wVPPw3pdwW1Ub/8FfjFu2HTxvP4hngVBw7A3r2BsFu9+sLNeyYOHMizffsM99yzHEV5pXUvmYD33/4mLewCUa7Ctpcu2HRfAv4H8M8vG/tD4Ekp5V8KIf5w8fkfAO8Gli0+Lgf+Drh8USz9Z2ATwaflBSHEw4uC6e8IPGDPEgisdwGPnmGO88L5zCJsBZxFcRUGbgQ+K6Vsf9k25Ya4atDg7YmZSLD89ttZduutSM9DNc6+ma+iqoSbm7n+A3/KruP/RGT82wz/Q43wVJnVP3iQ+ctuw++LMXd5BaseYvrFLkRUImoSYUu8iAq+QMRdQpEqni4wqWOWbHpSJxkr9yAdhfXJvbxU2kzanKOoJ8BWUEsOA/kx4tkyHa3TjPd1org+3hGdqhnmxItLqY7FkUmBe8JEDVuELqtQPxKlvTJJrRjBVk1cVyEZK5GM5MgmWpE1Ebj8cCGlgi4RuoeQguVE+NDVGkd9h//nmEp1dDHoXfjgBpmUvq7g22AaLvG4ZHpG0tEm+cQnVLZsUdi6VbCwAOtvqrKrLU/TUR0nP0H2YDvS0KFNoVqHqXno6RIkU8EUmg7f//6ZBVa9HvQ27OoKRNkbYW4OvvKVIJ5p9274j//x9AH2F5r+/jiRiPYacfVzxQUqNCql3CaE6H/V8B2c8m7dB2wlED93AP+8GNf9rBAiJYToWNz2cSllDkAI8TjwrkVjTkJKuX1x/J+B9xEIrNeb47xwPi1YHcB9i3FYCvAvUspvncf5GjRo8BZEUdWgFPjPQGt0BWv7b2PEfgKl3aS2v8jq6bzFKRgAACAASURBVOeofjXL6EfezcTadRRnkqQ6CxwtL6c8F8NstjFcB69FQWYkCAUNF1OtU0nH2G1eii81uqvj4EI218yYPsiKgSP4cY/W7Vm6c+MoYZf2yRmaF3Lsa19LrRpnKtIGCwoxUWLebw2+2YSCkBL1uIsVDWFsmMEtasS6iqh1D7+oYTguVgT0apWQWqRkdoIBnqojCzrjoxp/eAjKZZXNN8JzL4KT9VA8D6eo4hUEwgMtJtmwCdJJjVKXJLlSp3+9iqYJWlvBkT7za0cZ2Z4gPfwsLDh4YgM5BkFIfFUSTinMFGBqBjrbQNegUDnzdXjwQdi1Cz7+cVix4me6lD/GsoJ6XclkkO1oWW/seOeKSESjvz/+Zi/jTSMWhi3rzs2xvgItQoidLxu6dzHs50y0SSmnABZjtDOL413AyZdtN744dqbx8dOMn2mO88L5zCLcA6z/Cds0amA1aNDgjGTv/R6VE3uQNighaO+yqY6NoNz7b2T+yiOfSEO/grNPY0/XBryEQrR9IWj63FSgQ51i3OklalSYV5pZqDeRiBSpqSbPWldRcJtRhOC7lZuIm2U+Kb/AaFMn7blxYuE6y48PU0w1UY4kEApEEiVKMyn8nIKQoIR80CG8usZMsZOu8AQdzRNEIhXG7F6qZhhr1gBUnEQMVzXBVIL4KgTdGYWjJdi5D1KuwnN7wVWBDgFxBSOiEnd9xDwMLBF87ncNFiQ8OgVNIfjqEfijy2DtWpBxh/sdD//xMlq9wpy/FFFzggAsXSWWytEcr7Aw3sr+4QhtLQrjE3DdtfDkkz47d0quv15w2WWvjI1rbw8EUSLxxq9nVxds2QIvvADXXBO04DnX1Ovw6KMwNPTWcUG+1SlXYduL5+xw81LKc9X2+nQmRfkzjF9wGpXcGzRo8JalOj/P9i89gBp10WVQE8utgyFtWv15Vhe3M13phapCtTPKsbkhauEwahKazSK9ynHa5SReSMPWTBypsiO0GcO2qflRRhaWofseJT9FoVghpDtsXXstN+36LgU9Tt/8DKJTY4k7xYnmJXSWJ6ksDbPPvwTVsZG9PkpSIoTEretIT2fv4c309xzDLqkoPXXsqgmmGmRouToyoUM0aJWjGoKJKowUQS/BwTzIHxUrDSsgFGzfJxsBtRmWdgsOjMCzh4FlUPMga8NDo3BNG6wc1GgvgOyG2nCShDfFgt4NSQ0l5hDKWDiijKq67JyIMfJkB70roTApeeorPvPzgj//guTqdZI//rTg6iuD63DTTXDjjW/cPQjBMd797uBxvpifh23boFBoCKyfijc3i3BGCNGxaFnqAGYXx8cJiqH8iG5gcnH8uleNb10c7z7N9mea47zQEFgNGjR4yzK5cyflUtCfMJ4B14bsKDQPqbR8oA0vb6KWfPyaIDMwy5+H/hOfzf4+M0czDHQdocc/Qbo+TbSnyj55Ca6IEVbrOGGNihUiGcph+VGikTJrk3tQpUOhM8Eu9VKue+xx3PkS9vpuIr5FZ2eBpvk91OMGJ6/rQqg2FeJIVyM8VyNsSsJhh46Wabo6j5M3Mxx47kr844utdEIi+G0dIygs6oGwoRYFR4I9S/A7OwrkCQLdawRtddrAM6FYlzx2SNCtQ0czPJGDVWnYl4MDefgPq1TebXbxwJbDTOVW4c/5lLOBFySiV9FEnZKIsuKWg6Q7suzffxnHZw2e++cWpk7EflyF/js/lDz2DPz2Lwn+4PfOTezVhaSrCz71KUi/tkd2g9chFoEtG87NsX7GQqMPAx8F/nLx7zdfNv4fhBBfJQhyLywKpO8CfyGEaFrc7mbgj6SUOSFESQhxBbAD+AjwNz9hjvNCQ2A1aNDgLcv8kSPEu3sojk9QnHVRNMhNQPv6CGFLkpqep1POcdhYwgnRR1nJUfTjNOtZmHAw3TJmcx1hL9BpTjCjtFEVcTwEg+lRipE4c7kOFN1DxaXmRTEUi+mOLsQNCpEXHTynSlmN0eKOojfp7I9rrDIFoyQp4iJ8ixVRnxsv+wFHXY9jx3s4sX8ts06CueE4whPIKkGCuQ3ECYRUAvwoSJcguNghKFBqETQY+1FZB4sft+HJ1hT0DPzR++BfJ+DKMDSZkHVgtAIjJbi0RePu9Aj/raebhb0p8FVQwa2qaO0u4Y4yy9bsZc7uYeiy/Wy/9wrmJ8wgMAoBvgz+ePC39/sIX+cP/kDQ2Xnhr//PihAwOPhmr+LiolyBbc9fmLmEEA8QWJ9ahBDjBNmAfwn8ixDi14AxgpJOEGQBvgcYJvgU3QOwKKT+FPjRqv/LjwLegd/kVJmGRxcfnGGO80JDYDVo0OAti6qqdG7YQGVmmtzRYTQdUOD4fofkbTalkMkJcwXhkEPMLPG9mWup5MKE9Cq1RIqJUA9t1hhywqHH24syBEeVJMIXuFJFEx5JCszX09QNkwgVmrw8TRTo0yYwBlS6FqZ4tpDhmWWbmApnUNwFmt3dCCtMuNbFMi3EXF3n+wczZN05zHgVq2gw/VSGalkgiwSV3uGUaKoAJfDCINKgZcFNEwipMYLCpT/O6FqMh1qs3tCVCqwNhgKOD2MW7CzDgg3PF2FDS4R7loXpT32Dz07fwPHDXbiqge+CCHl0tRxl5kSG+UQ79oiJqdZRIx6eq4IbZC3iADZ4IcE3v+3Q2qryx3/8syUqNLiIuHBZhB98nZduOM22Evjt1znOF4EvnmZ8J/Ca6m5Syuzp5jhfNARWgwYN3rK0rVtHaXqa9R/7Nfb8/d8wd2KSkALKZJ3ClE/fhjqD4hj5cCsaDn5GEM4V4XgebfI43tRJor17qK9qxRvoYoOzk5RWYE5pxZU6sVCRPWID7v/f3p3HyXHV997/nKrqvXt69n00o32zRtbqTZZ3YzvGcQBDwmaCiVlMbghcCJDleQj3uZc84XlIuAkh5HLZ4hgbA7Yx4GAb2+NV1r5YuzSjdfalZ3rvqjr3j2rbkiwLLS3Not/79aqXZqq7T58+Gk19dc6pc7IBdvfMpzlyiIiRYZrewpBRhb9aM+yDf5/xR2SCMfrHaugJt6AM8BdGmbWtm0GnisHqdsYSYaoCzSSPptj66xDZtOlloyBecLLw/v/dhXdPk4E3ZDgK5i5wcqBzeL1cg3jb5zh4Q4R5MKMwvRruudxrm2saYFcCdoxByoG6oNfZpTCp4w7eUzNK5V/tY9PaI6x9sYoDCYVq6ycTiJItq0IlIDMUREUdaq/tIT/qZ7CjCmfQ561Ir1zIQLfP5Ve/Mvgv/0VTVjaJxgnPQTrtMjKiKS9XhMMXx/5A0TCsLtG0dNmL0CMBSwgxYdUvXsyB554DYMXnvsiuB35Az7pNLJ0F+Ye76W5bTGJhE0fKmhhtiPIR5yUMa4hfjuaImPu43fcTjqaihK08BV+BhKplttqDq0wKWDiOSWPNQeh1Gc7VsD83mxnh/SyxNuH3FehT1Tw18xqaQ4O0JLeSzJfx68DN7PbPBp9LYbrF5dsOE8i2Mwz4tMnBbWXeXYAVeGHJAWrwpul2AT1AwkGVuUSXjmKPBTBmGOjDfpyAgTNoQAIvmEUADSEfvGcl/On1MK24mnhjGO5Z4LBhwOCJQUU8ADfWeI8pbeBzHEy/zcpVKS6/LM/2wRSP7tLeXtRZh+xQEH9lBiMZI3koiGk5+GM5MgnLG7f0ARkHpwBHj5r09JgluYtwItNa09GR45FHcmzYaJHLKj79aYMPfCDAqbZ2mwqSKehYM961mFokYAkhJix/NMqSe+5h6/33M3q0m8brbqF65dWMJnYxt+Uoxt4EvWVgrCpnWTDBLFbT0r6aTy/MkuhdSPfaTfT4sqyb0cC0pgQp3yC6AAvVZoYHgnSbjUzfvpOZB0fpW7CI1LRGTH+GH5rvx4o6WMMZ2tlE0KdJlMdIOX5uSj5Nr6+OpAqhLM0tt1/Jw+u9qUupJKRCwGJv0XZXAQN4YSUALNEYiTxRa4zA3CxGJRixHM5rAeyIg5FUzFgcID2qyBaypNN+0hkfS+sVH10CK6a/2TYdDLE1OEp1s5+/a6zDr02Cr4/iZV6GsZ/RHr2KNZFqugdCZJMRZpfHOZg5gE9lMGttQq5maKAGQ4EVcNG24Q0TKQVag2sTLBjYtotlvd0d8FPH7t02P/pRht885WegL4Dl03zxSw7z59ssW+b73QVMdhdoiPBiIQFLCDGhRevquPwzn2G4s5N0fz+m30/59On4K3zk6Eaxk3JGidJEAysBMM0glY1zCTf8CZEHv0L5YU0ucphdt81FBwzMownyj/dQdeNy7Cd3ERvoo3rzdhJzZuG+Yw6j5dX0dkV5n/4emfkNmDpNg+pBWZqRYAU+chQoJzbYTWV1jhXN8OIB6EvDWB1YKSAPtgFOrUvZkQSBpjz5WhNjiQ2GRgcs7Dj4KzVldQ6rb0yjOw32brWpyhj4y3KMleVQ3XGWhC2WHrPJcAqbrYxST4BucvQbOWYSfvMJKgTKT63RzIz+hTz5lIHp+LlmocKaG2FvtoctA1Fe/XkrShuEQ0OkjwS8oUzn9cPGNMBSPlpbx3+1da0127fnWbs2i+PAkiUB2tsDJd20eu3aHBs3a9JphWG62AWF7Sh++ev8lA9Y0QisXlmasmSI0CMBSwgx4SnDoHLmTCpnzjzufJjZTGf227wKgitupuJoguwz9zN6+QyW/eMOum6sobe+DHYlaN7wEHZKEdA53DVZmu/fwNb4n1FRU6CqL0tgYYFCrkAkl8YJmQT9eQzTwe11mHVkP+99ZSvbzFZeKdQxmoM+x1tZwTUBPxhZhzqjm5npTq7sW0M4l+Q3tTfx2qzZ2CYYysUy8mSDJrsDimuv8HHF1UlyjkPAzGGkg8waqWB5LUSOub4HMKnARzc5TBTxE3+Vh5ZCYAEYQSJjEMbrTYuYcGtFG9CGeyd8X8MjvwXXjbDiphTdc10e+JUiOWSiCwYBx6a1VfGZz1j4fIrnn3d55RVNNguLF8Pq1Qbl5RemV+s//zPNM8+kqagwMQx48MEx9u4t8N73lm719WzWW1m+qtphZNggn4f6hjyuPfUn+CeT0PHyeNdiapGAJYSYupQifud78S2/Aje9nv7re4ibPdQ+/1s2buiHQop0tyaZBsuBxrCPwEiGm7Mv4Yxkaaof4EjYj6MMzJRDqjGI/2A3v9fzGAvX2+zZXsnh+VXU1MEdMzM8/eIRsrWNjOgwroZIJEVIZ1nY0oO/0sT0KWqcPoK+meCAYeUJ2C4KqLYG2Om04lM+KjI93LZnI/VWGebc28B3fIiwUNxJPUfIUoGPak6yz6MRBKCtFt5/NYymYfkxO78aBnz03fCRP3j9+wgAq5bnePDhAnZO4zNg/nzN7bf7+PGPXbZv93qywmFvJfYdO1w++UnjvE9+HxlxeP75DG1tPkzTe6943GDTphxXXRWiqak0l7LLLvNRU53jwGGHWNzFMDSRiObmm4MlKX9C04z3QqNTjgQsIcSUF25uYQEtONgoFM4ffAky3+XgE88y0v0kRDP4kw7lrsGSgT2oWihbkiPkZph/cAddbdNIZ0LEe4/Q9egYsUM5MocbmXtNOyPN5QBY7hjXZTYyJzXMxvkrOJiH1l1ruX7bw1gzFuL4Chwob6RgWpiOIuSHoB8C5MCAiAVRpdmZKbC8fycJZdPU+xrYGVj6EcCbFvXTXtifgQ83mswORMg6sHXMu4sw6MLja6FvBJbNglULvOlU7W288fqj/VBbAVbxt79xzE1yWmt278hz6w0GluU9cOCAw7p1Ljt2GLS18cZk76YmOHRIs26d5vrrvXN9fd571NWV9u9vYMBBKd4IV+DVQyno73fOOWAdPZrjpZdGCQYNPvFxPw//tMCRIzbKyPHxj1Vy+eVTe3gQIBqF1ZeXpqz7/7U05Ux2ErCEEBcNs/grz8BkxXs+RtuCK2latIKx3l7S2Qzlv/gRZZ37SdS2UGelyeog5liBaV2H2BVtIvfj/dRGo7TNT9Koahjt3s1IsWyruob4dddQEYlwSRgSmR7SP3uQ8r0bSfpNrKZG8n6TQsCHEXYxDQuHICPaJKptYiqGoSCXL9CVKWPQVhBxGOk7yhPbYFEtzK6GjWOQdGBvGqr98LPDsHEYYhZUHoS+YaiIeEErHob2YybG7zsC3/opvO9GuOxttpBRSqFP2Lmtv98LYifeSVdeDnv2aK6/3tsM+qGHvPPvfjcsW1aCv7CiWMx4S53efOzces+SSYfvfrcXgHxeM21agH/7Ti0PPjjA5s15rrnGxDCm9uR+KA4RvjTetZhaJGAJIS5Kpt9P/aWXUrd4MXYmgxUMsru9HePbnyAac3AXmZiVit2ttfh8DoNDUapvzxEKakKRAP55+0gGbn+jPKUUvlpvWxptJYjXv4B/iYEZrKU5GsQY6KFjzhJ2V88hkkoTiJVho/Djx6f8pFwIuZqD3YrR3jIeeTrB/MU9/DZ4DbsG4LV++PIq+EADvDAKj6fg5RwEC2ApyLtwYABm1oChIOyH7uHjA1ZdBVy3FKY3nLxNlFLccIOfX/4yR1mZIp3WNDaatLWZrFv31oSTzXo9WQDbt3u9IK9/XcqAVVdnMW+en5078zQ2WhgGHD1qF+t2br1Lw8M2uZwXrLTWdHVlicUUH/pQFTffXMa0aYESfYoJToYIS04ClhBiQutnC6N0UcdyopR+vxalFL6wdwfe3Hs/jnPZPg4+/gN8z43Sf3cjwagmmS+joSxFrsJPsODgKh/DVVnqA89xuHALBX38AlHaP4gyIfye96GNPhLD1/F42mZXXRpbKaLBDDYGJhEqfQoHGHGArIPtKsqrajBzFoeqF5OvvoRMP9RGwGfCgii8kIEKBwZtuK0G5kehNQxrBmH7Ia/nKlPw5l8dKxaBd1596va4+mof5eWK/fsdyssNLrvMCzDhsGZ0VOO6mkIBysogmVRcdpk3lLh8OezYwRtfl9pdd0V59tkMa9ZkcRzNsmUBbrwxctyw4ZlyHM2+fZpMxuTgwRy27XLJJV6Z4bDJ9OlTf3L766JRWH1lacq6/y1rq1+cJGAJISasAml62YBFkB7WMovfP+/vac66h9Y717H537ZS+eQg9mITX6BARpmUlRfwlZuo4TTataBwBJMchRPKUIVyNAqCg+hCmP+1z8/+dAQGIwRmZ1DRBC3BvWinnjGnnpwLUR+YAQMCmvBaRSDcRP2CRhbWGlw56gWs4rQoloThkRGImjA/DDVx73zzlVC1DQZG4dZlMLf5zD+/Uor2dh/t7cf3DN19t8E//qNNR4fD4KBDdTX8j/8RYPp0L+DMmQN/8RfeHKxY6W7se0MwaHDLLRHe8Y7wG/U8V/v3Ozz6aB6lYlx9tUssZrJsWfScy52MkknoeGG8azG1SMASQkxYFgEi1JGil0rmHf9gIQ/ZJITLwCzhr7LIXIzpf07jux5i5292kCizCFo5fPECtf5RRnUclYeGwUPsamgn78TfUoQqVEL/9WCN8VRnFbsSPsorBrl02gb84RwjlKEoUFADDBXqiwFNkwtqbm/w885FFUy7MUh9vTc8NaPi+PJXRKDZB8k8+I4ZuQsFvGB1OhIOrMtA3ISlQW9Y8VRaWhTXXw+7dhWwLBvT1ORyikTCIB73kl/0hGySSrn09BRoavIRDJZmy5lSrqje0GCwcKFFba3BzTdP/dXaT0mGCEtOApYQYsJSmLRxMzY5fMcupDk6AE//b8iMQUU9XP/HEAi/fUFnKvR71K2ME6p+jKFclp8eTpOKxvmjrp/QHOwmOJijs2IeHc6fUnuyJRIAVaiAQgUbxhy0crh01ga0rVADmrnTd5FUEXqZR950CJhgK2jKWXy5ogLmmWw7BHt3wux6qCs/vuyeLHyvCzKOd128owFWVp7ZR/xhAnoKUNDeKvQrT6P5liwxaW5WDA5qZs502b7dYWQkR3u7n+Zmg3j8zYBi25qvfKWTLVsGueqqOv76r1vPrIIXQDRq8LGPlfDnZhKLRmH1qtKUdf+PSlPOZCcBSwgxoSnM48MVwPYOyKehugX6OuHgNphdomWoAZQBwdWUzb+CmDPAFU99h5/vvpT/J7KKtsBG8knNmP+dzAq2v20RY67mcdKMzs9jdfmImElayg4SDmSo8A3SbPWx0bY5pOdSa5tUOTkKwT08afvYvb6Bgf4QZjbObzaZfOwGmFbzZtk/O+Itv9AS9ia3P9YNc2MQP8353q6GfhsafNBrw/BpbJEyMAAdLxh88UsRUkkfSikaGy0eeaTA976Xp6XF4M/+7M0J4YWC5tVXuzEMl5de6sF1p10Ud+NNVskkdDw/3rWYWi6ObcKFEFOLaYFbTAVae4HofFA+lNXAog/eSyStsY7EGNq3BLPyvcyrWk6Iky9Aeaig+dfYMDvrBwlGE8QX9lNV3k+sfAwrWsD1m8RVjgXWThr9Byi39pCP/oxI4GW2j/wGO/rvVM99nrKlL+GLjdGx4/jyh/JQVvzvsd/wdghM26f/sQwFd8SgtwBWFi49jf9qdx6AZzrg0GFFLqf51reGefXVDPG4wjAU/hM68kIhg/e/v5lo1OKP/7hZwtVkoEt0CEB6sIQQk9GCa6CvCwYOQNM8aF10Xt8u1tDAZe++ldyeHM0V1ht3HZ7MiKN5MN7DcMLB7gpghgtEGseor+wmqcKUMUZUj7HXmMF0X5Y5+jckg0OkVSXayWD70pQnwsQznRxpqyQ3YwOJxBKyhAkWf2UvLIO1w9AYgpFCcQ2sk49Uvq3lIRjeCk+9DI+3wj13nvr5l7bD5/4MWqdBOu3Nqfrud0f52teqaG/3U1//1gB1771t3Htv2ynL7ezMMDJi09oapLJy6i/oOVFFo7B6dWnKul82IwQkYAkhJqNIHG79NNh58AW88bLzbHFzgOePBPCdYsrOUeBBY4jeTpP8UBjygD9EwfXTWT6DXdkFONqiMjDA7VVP4LN6qbbCkE+TGAlzIDWDeGqEMgYY0jEGzDT58kFqmoP8iig3MJM4QW6r93qhdoxBQwDuaITAWawoMDwCtgO9g8WOwFM0o88Hs4tb7fj9JvfdV85//Ie3+nl9/Zs9iPm8y6OPJrj66ij19acOTBs3jvHgg30YhiIcNrjvviYqKiRkjYdkEjo6xrsWU4sELCHE5GQY4L9we8Q1lkFzGSSyED/J2yaBp9I5eg7mGOuqRmcMCIJVVkAXNM9U3Uh1vB/XZ3Kk0MS6VDsqUGDr4ZlsPrIYn5En7w9SoIz1FqxoSRGuOkBD0MfsYIQ0Lus4zA3MImDC7zdyVotWuC68tBle2wfV5XDHNTC39cwzalubjy9/ueot501TUVtrEQqdfNjWtqGrC6qqYPPmJJWVPsrLLbZvT/HccyPcdFMlkcjFs/6UmLokYAkhxGla3Qb/vuXkAesg0N/TQ2p/NTpsYCiNyrgQUOiARc4MMWaVYRneZKm99myOJhrZv3kuuXQI5Wpyyk+8MUGk2mFrOsoK5aMqGMJHgDAOY+Te8r6OCzvHwHZhbhkEf0c22bATftEBNRXQdRQub4f66tNvg0JBs3Nnjtmz/SddesE0Fddc8/YLYT35JDz7LMTjsGRJgB070gwP26xbN4ptO3R2prnvvhb8fpkifCHJEGHpScASQojTNK8a5lXB/hGvN+tYY4Bhj+LoOiyzABa4cQMdUPiqcqiQTUpHUQWNi0HQSXJ4tJWRUCVmwcGfzpPKR6kcHmBR22+JRQpYWYOK+HI0mhGyzOStPUaPHYWXB7whw1lR+OiMU69pdbgXyiLe4TNh/+Eza4MDBwp8//sJ7r47Tnv7mfcgptNeL1o2CytWxBkYcFm/fpT580O0t0c5eDDL8HCBurqLZIuaCcIbIjyN20nFaZOAJYQQp8ky4X2L4Hsb4FDCC1mvD63FgWBzhLCbotDlw5lmEo0mWbRoI/G6BAm3jMOFFvJ2mGhoiMGxGlJujDw+dHWQquQAS9UG2ts2sqd/FnmgKpAj467ncH4VgVw1EauBXAgCx3TubByG6VEwFXSmIGVD7BTTmGY0wctbwDRheBSuPcNtbaZP9/Hxj5fT1naGs+qLbrkF6uuhsVHz0ENJfvELjW1HKS/XHDiQpbExIJPdx4WsNFpqErCEEOIMhHzw0aXw8HbY3gdBC2oi0GJAeaSJslVH8F+SJRMJsarqBa7KvUJ8dIQu/zQ2xtrptNqwdQA9ZmKnTdyIn+bAQX6v+lfMdXcxPdJJyM1yILWEtApxcCjLEwdbsSN5fhrppDkR5KNllcyK2sQJMydmsSXhklQFaoMQNgOMZiHvQHXkrfVfNBvea8OuA3BFO1y1+Mw+v2kq5sw5+96lSASuugqSSc0jjxQIhUyUgnS6jHvuidDUFMTnk+HBC80bIizNzSIyROiRgCWEEGco6IMPtMPhUXj1MGzq8RbvnG8E2F8Wxm2waVBHuSn7NPVOL0krwmX5dUTTYxwNN2LlNLV2D0NuDYayme3fTdoJ0uvW0ewe5MrQKww6rQz5QFvDRKsP4gR8uAU/XeYQX8n1cIXfYZ7fz+0tl5AKJ9mhxyivzLG70MhPX4mQtuGeJTDzhFFFpWDZAu94nW3DC694f159BQQuwOhcMKjw+w1SKU2hoJk+3WLGjNKsqp5Ou2zYUKCuzmD2bOkNOx3eEOEZLKYmficJWEIIcRaUgpa4d9w8yxsyzNpwrR3n/88fxTY0TfooY2YMB4uc6WOG08WCoXVYr/YyRhnd1fVE42kWNW0ip/yEcmNYowVaQoe41H2JHb52Xui5jt3ZJhws6mv6iIXTWKrA/nyEYf8YlnmImbVBTLLYuIymbbpTsGkAsn74q5VQ+TumSm3fBY//p/eZQiG46rLz02Zr1xb4+tczBEMGX/m/Q/z3/x7jG99IEQzCF75wku62s/TsszmeeSZHIKD4/Odjb+yVKE5FhghLTQKWEEKco1gAFtR6Xy8lxLKRX3NQ0wAAFmRJREFUUb7d9TL9NZU0F7rJGCHijDJAlNq1mwms6aIy5+O6K2z6FlxG3rJo013kA352lc3C2Zum0u1lXcWV7HfmYfiT5F3F4eEq2sJj+JXBiGMyJ+qjlwK1RJhOlAgWS8MxXmoANwT+AGwfgVX1p65/OOTNL9NAtHQ55y3+9m8zjGVgf59F/+dyLL3EJJP309qqsKzShaCyMgPThGhU4ffLCvKnIxpVrF5dmuUxZIjQIwFLCCFKbKZq5S/33cejkfk4WlGmRxjyx3nFaSef6WOgaS75cBSzyY/Pn2V/oI1AIUct/TiHE2zeXUX9mh30fzSCChXQ2gBD4zom6XQEn6WIB0zmU4EG9pHkQ8zAxAsT10/z9hq0FEyL/u76zpoBn7wHHAemn8c9mU0THBtsR/PsDotfd5qQs+AXNvffn+b5FyI8s81g1yG49lLvOBtXXeWntdUkHjcIhSRgnY5k0qWjIz/e1ZhSJGAJIUSpqSAh/0qae/ayt2U6+c4MAyMW2Wk2RucIiZqF7G29mpraFKNONb68wx7fLHwjOYK7j6KjYYacMoZ1lIJjYThBbLMAhsZxgvgNl+aQwipuJ5vSLht1nkuUn6BSLK6G5ihYBsRP82a/1pbz2B5FX/lKmG98I8tARNGdsiAFVGhwfezdZfOd7+dJVwZpqYEn18HS2d5yEmdKKUVLi1zezpwMEZaS/AQKIUSpRWvx161i/nNbSOS62d4fJVFZhfvbQySr6thSdwtHxxbiH95D+mCQUGOasYYytuUWU3FJHfb2EfbdchcBI07S8eNqk4BpE/NlqDVd6qMu8wJecsrhMOj6+IGb4R7T4FLlTequunCL3J+2xYst/uKrUe74v1xvZVatoYB3JdKKdBKMKugZhkgIAjI//YLxhghL0+AyROiRgCWEEKVmmKilHyLkT9I98ip9G0fIB/qxRjMMf/AaDqZW4q416cnUM23OQfLdPrJGEK0UY5HZ7AgvYFSXYe8PYVTmaJ/pMJrNETcGCQ8HuK7OohwDF80gOd5h1KAJ0YbJqA1h0xsenIgCPmisNNh7wIGYgqyGwzam6fCpP/GRVXCoHy5pg8DZLbUlzoI3RPjWnQLE2ZOAJYQQ54Plp+/Sd3AkDUORFOUbtxNvDWLMGsX3Uh5tG4wkKon1jRGrSBDIFxhKVXBg/RxSQ1HIAAVwa4JsyuYom2bREowRtsY4YttsM/IYSnGpKudSVUavbfKtfki7EDbggzUwbQIuhp7oheVV4LTA1hdyJHugIubwtW8GaGnxoRTUVUJQwtU4kCHCUpKAJYQQ58lrfcPYg/20WYM03pinq2oBvdkGwqFRxowqLG0z2F/D0c5mqqp7OHpkGoWRoBeuxpR3W18aGPIzqkw2z04zJz7G8xqsvEPM9RO0TJ62htjQV0PQgGo/jDrwoz74XBOcZLvAcdPTC997ANZuttn6mk1txOWxJ/08vS3E3jFY8xpkNPxmsyZgwCdugfrKCdoVN8V4Q4SlSeQyROiRgCWEEOeBncux+f/9Fq2zjrCi8jVebL+NPf2zeTm9imwsirU4R26PHzXkUF95lGjZCH27G3BdH442vEWpTEABjoadBoU6H7kWTczt4+bACxg4DDtzeSV/EwVdRa3ppakyEw7ZkLBPryfouefy7N5t8973Bs/rmlGpFOw7DGvX2zhph85BzR+8J8sH/9wiEFD0DMK6w5qtr7isWwM/+2f4h68ZLJivzmhD6lLIZl02b86Tz8PChT4qK0uzhMFE5Q0Rpse7GlOKBCwhhDgPejZvxvfCZqZVpum/rpWq+gSH+6fhyzuYeQdqNFaFTWYszFhZgMFNreRGwpgzCjgEYACq0v1UBgdJqzDDqhw9amIYDkvN9aTcAI4KUWPuJuW2sZ8IWRdmGhW4rjfUFj3NTLBmTYFDhxxWr3bPa8BqaQbL0DgpwHEhn2U47fCrn/lYtCxIQxAsXF58VpFJw1gKPvAZzT0fVXziPdBcd96qdhzb1nz/+0kOHLAxTUVHR5b77otRXj6VQ5YsNFpqErCEEOI88IfDVOWgZ6tD7afD9Idq8GubWHCUtBNG+R3y/iCua5BwKzEqDNwZBm5DEHwuZjSPvztHaiiKvzzL4pZN7Bichx8bE00eExtwFVT6BrmsaoCOgTjDKkGzruauaoicIg+4LmTzEArAhz8cpK/PZdas8xsg/H5vDSwsA1QBQgrGFPt2JLlmVZD+fpjVAiQ0UVPhWpDKgO14x/nW05Pnt79NkMvBvn0Gs2d73X9dXQU6O22WLJm6ASsaNVi9OlSSsmSI0CMBSwghzoOahQtZ9JlP8ezT3wZVRaUxTNRNUh4eQtk2iXwFjmnhy+TJpsP48gVUpYsOmRAAFdWYOkfMnybWNEZ5IEEoncYuWOz3zWCZsQlDgVINRFUjjRGX+sAQWdvHO61qyk/x230kCT94CvoTMKsJ3n+NSX396YWHbNalqytLS0uAyKkSXFHXIYiGobq4J2Imp727BystSLnQ6IN6xWNDLndGDW67xKDqMpdDa8Fy4Y9vU9z7LmhrPK3qnbX168f4m785SFmZSX19gP37fbS1+TBNL4wGg1N7Lpg3RJgc72pMKRKwhBDiPFBKEbz7WmJ3HWHAHSJYyBMOjKELilVHn+X5shtIJaKAwpd0cPstVJ2Ddkzv7kGfQXRWkqr9I8RiwxhpqGsbY2XsCA2mnwIrqKQZaCKKwwgj+CyDmp4W/m095Apw2Wy4/hJvBfVjvbgdBsdgWi3sOAg7D0P79NP7XD//+SDr1yeZPTvIxz/ecMrnJkbhX34AzY3wpx/1zt19t8EzG1xoCUB/Dub5YL9iqFfTWaOJ1iluuddg9FaYUwt/ebtCnedsMzpq8+MfD5DLuQwNaRoafEyb5tLTY+O6ihUrAsydO9UX5ZIhwlKTgCWEEOeL6xItpMlol1zOYn54K/uPLmDYqsMsuPhTBexgHrtgEQraZJ0ATipHIJpHY9OfrWVW1T4cZXA4P5Mr546xwJxOBRGCxFD46CNHE9W0M4PObsUPn7Ooi0PID09vBcOAGxZBOu8d8ZA3bx59dh9JKe8wjDdTTzLpEoko1AlJKBaFW6+H2mMmqN96leKG1RZPb3agOgLDChxw+zXrntB8dVhh1itcP7zvnZz3cPX6ZwoEDFpaAuzcmWFkxOFTn6pjwYIwtu3tbTjVRaMmq1efxr5Kp0GGCD0SsIQQ4jyZabRwVFVR2LyT3PRKyiOjLIlt49m+a9jrzKRG9ZBJhsj1B8AyMYfDpH1hrFiOYCzL4PpKXqm4ggorzbyVe1hSOcwybuMFEsRRFMijUMwiTBAfew5DOOAdAA0VsKkTmmrgxxu8eeXlIXh3O+zrgQN9cEkrzGs+vt5a67eEpdfdeWcVS5dGaW725icdOGDz7W+nuOuuIEuXHn+bv2HAtVce//qNr8HyhbBtLfSud6FgQBQMV5PKKl4yNB/+Q4VpQmEUqC3F38SpxWIWd91VxXPPJVi9Os6tt5ZTXz8BFxE7j5JJh46OxHhXY0qRgCWEEOdJBTGuidzDfjrp/u4z5NqizFGdzIts4qqBl3n4wHtYu38lhbSfhX/4GgP5WnRIk0v7MPog5Y/SOOswV8zawfKqUZapKqoIU4fNMHnaCLKccirxwk7QDwX7zffP2+D3wUMbvWAV9kPPKHR0wn23e48fu4yD1prHHiuwdq3NqlUWt9zy1jUegkGDOXPenAwdjxvMnWtRW3t6c7jiMUgmQRsGQZ8mX1BYGY3lM8hpsGwYSsAlc2DaqUcgS2rx4iiLF5emB2dykiHCUpOAJYQQ51HcrGPJqm8yb/ZGfvjtNJ2bH+OmS76LE0lxaF8LvZ0NGI6LOWJTHe4lZ/pomN6NHQKjwuH3q9bRqsu5UoVI0MyDjOAHcpiECVHNmyFoxUxYtw8ODHg36tku3HUF/GSbF64AyoIwlPJ6l05cI2twULNmjU1zs6Kjw2bVKh/R6KnH6MrLDT7ykdPfkXllO6Bh+zqXLX0K7QNQOMVr+7QW+LvPQUXcq6O4UCRglZoELCGEON+MEKGGKzGaHTLB6/iPNe2sHXZYxzLvLroqxcs7V7F48UZi5himabN/eCaxWpu4vQiMo1Qwh10sIIqmAgsHzQbSrCb2xttUROFT74CtByDvwNxGaKyANUfg0LA3/2ogBTfPO3k1y8oU1dWKQ4c0TU2KUGnu2j++KQy4fInibz5v8LkvaPbuhXTBu7xPmwH/8FVFVUXp31ecmjcHK16SsmQOlkcClhBCXCArVyr+5//WdFVezuGhEaxaB9vvg6Am5UZZ84srCEzLUrFtBD3NYXFblox/CU0+RT0mFYyxnzQVQAqXGG8dliuPwNULjj/3weXw1C7oS8LKVrhqxsnr5/crPvGJID09Lo2NBqZ5/maYX3utwQP/rnn0cZfOA3DlFQa33qyoqjxvbylOwZuDNTTe1ZhSJGAJIcQF0t6uuPYm+PsnGjhSX4trmxAojoOlHTBcsukgRzY1M2fBUS5VTaSBq/zec1YQ4QgFjlAgjMEdnF6PQyQAv99+enUMhxUzZlyYBTXnzFF8/rNTd/HOyUUD7nhXYkqRgCWEEBeIUooP/J7iaEOQf/5lN8mXK71rWkGDYeKETRgBtMuM+gaCUcW7ggYtxZ6kMAZ/SAUZXAIYWEztxS/FhRONWqxeXZqxWRki9EjAEkKIC0gp+NSiCJt6wuhD3Rw9UIMz5PM6EAzvaF+u+IflJjMC4DthuQQDReQkQ4NCnItk0qajY2C8qzGlSMASQogLLOqHP13SiJnPsfFZTddrilxBYYY1q1coHvmyQTg43rUUFxe5i7DUJGAJIcQ4uKIRaq4L0DEHDgxByIHVrYorT3PLGiFKyRsirCpJWTJE6JGAJYQQ42RWuXcIMd68IcK+8a7GlCIBSwghhLjoyRBhqUnAEkIIIS5y3hBhTUnKkiFCjwQsIYQQ4iKXTBbo6OgZ72pMKRKwhBBCCIEMEZaWBCwhhBDiIheN+li9ur4kZckQoUcClhBCCHGRSybzdHQcGe9qTCkSsIQQQgiBDBGWlgQsIYQQ4iLnDRE2lqQsGSL0SMASQgghLnLeEOGh8a7GlCIBSwghhLjoyUKjpSYBSwghhLjIRaN+Vq9uLklZMkTokYAlhBBCXOS8IcKD412NKUUClhBCCHGR83qwWkpSlvRgeZTWerzr8DsppfqBA+NdD6AaGBjvSkxy0obnTtrw3Ekbnjtpw3Pzu9qvVWtdms0BT4NS6gm8OpXCgNb6lhKVNWlNioA1USil1mmtl493PSYzacNzJ2147qQNz5204bmR9pv6jPGugBBCCCHEVCMBSwghhBCixCRgnZnvjHcFpgBpw3MnbXjupA3PnbThuZH2m+JkDpYQQgghRIlJD5YQQgghRIlJwBJCCCGEKDEJWG9DKXWXUuo1pZSrlFp+zPmblFLrlVJbi39ef5LXPqaU2nZhazzxnGkbKqXCSqlfKqV2Fl/3tfGr/fg7m59BpdSy4vm9SqlvKqXU+NR+YjhFG1YppZ5RSiWVUv90wmv+qNiGW5RSTyilSrU20KR0lm3oV0p9Rym1u/jv+d0XvuYTx9m04THPkevJJCUB6+1tA94FdJxwfgB4p9Z6EXA38KNjH1RKvQtIXpAaTnxn04Zf11rPA5YAVymlbr0gNZ2Yzqb9/gW4F5hdPC72xf7erg2zwF8D//XYk0opC/hH4DqtdTuwBfj0BajnRHZGbVj0l0Cf1noOsAB47rzWcOI7mzaU68kkJ1vlvA2t9Q6AEzsAtNYbj/n2NSColAporXNKqSjwWbwL3EMXqq4T1Vm0YRp4pvicvFJqA1Ca3UcnoTNtP6ASKNNav1x83Q+BO4FfX5AKT0CnaMMU8IJSatYJL1HFI6KUGgTKgL0XoKoT1lm0IcBHgXnF57lc5Cu+n00byvVk8pMerHPzbmCj1jpX/P6rwP8HpMevSpPOiW0IgFKqHHgn8PS41GryOLb9moDDxzx2uHhOnCatdQH4JLAVOIrX+/Ldca3UJFP8twvwVaXUBqXUT5RSdeNaqclJrieT3EXdg6WUegqoP8lDf6m1fvR3vHYh8HfAzcXvLwVmaa3/XCnVVuKqTlilbMNjzlvAA8A3tdb7S1XXiajE7Xey+VZTfh2Wc2nDk5TlwwtYS4D9wP8EvgT8t3Ot50RWyjbEu640Ay9qrT+rlPos8HXgQ+dYzQmtxD+HF+X1ZKq5qAOW1vrGs3mdUqoZ+DnwYa31vuLpK4BlSqkuvHatVUo9q7W+thR1nahK3Iav+w6wR2v9D+dav4muxO13mOOHVJvxemGmtLNtw7dxabHMfQBKqYeAL5aw/AmpxG04iNfr8vPi9z8B7ilh+RNSidvworyeTDUyRHiGit3fvwS+pLV+8fXzWut/0Vo3aq3bgFXAbvnHcHJv14bFx/4bEAc+Mx51mwxO8TPYDYwppS4v3j34YeBMex8udkeABUqpmuL3NwE7xrE+k472Vq/+BXBt8dQNwPZxq9AkJNeTKUJrLcdJDuAP8HoEckAv8J/F838FpIBNxxy1J7y2Ddg23p9hvI8zbUO8HheNd0F7/fzHxvtzTJb2Kz62HO+OpX3AP1HcreFiPd6uDYuPdQFDeHdpHQYWFM9/ovgzuAUvKFSN9+eYhG3YinfH3Ba8eZTTxvtzTLY2POZxuZ5M0kO2yhFCCCGEKDEZIhRCCCGEKDEJWEIIIYQQJSYBSwghhBCixCRgCSGEEEKUmAQsIYQQQogSk4AlxCSnlCr5ZrBKqTuUUl8sfn2nUmrBWZTxrFJqeanrJoQQk4EELCHEW2itH9Naf6347Z14e/IJIYQ4TRKwhJgilOfvlVLblFJblVLvK56/ttib9LBSaqdS6v7iSu8opW4rnntBKfVNpdTjxfMfUUr9k1LqSuAO4O+VUpuUUjOP7ZlSSlUXt/NAKRVSSv1YKbVFKfUgEDqmbjcrpV4+ZvPf6IVtHSGEuLAu6r0IhZhi3oW3l95ioBpYq5TqKD62BFiItzfhi8BVSql1wL8Cq7XWnUqpB04sUGv9klLqMeBxrfXDAMVsdjKfBNJa63alVDuwofj8arzV52/UWqeUUn8BfBb421J8aCGEmIgkYAkxdawCHtBaO0CvUuo5YAUwCryqtT4MoJTahLf9RhLYr7XuLL7+AeDec3j/1cA3AbTWW5RSW4rnL8cbYnyxGM78wMvn8D5CCDHhScASYup4264lvD3QXufg/ds/1fNPxebN6QXBEx472d5bCnhSa/1HZ/l+Qggx6cgcLCGmjg7gfUopUylVg9ej9Oopnr8TmKGUait+/763ed4YEDvm+y5gWfHr95zw/h8AUEpdArQXz7+CNyQ5q/hYWCk15zQ+jxBCTFoSsISYOn4ObAE2A78FvqC17nm7J2utM8CngCeUUi8AvUDiJE/9MfB5pdRGpdRM4OvAJ5VSL+HN9XrdvwDR4tDgFyiGO611P/AR4IHiY68A887lgwohxESntD5Zj74Q4mKglIpqrZPFuwr/Gdijtf7GeNdLCCEmO+nBEuLi9ifFSe+vAXG8uwqFEEKcI+nBEkIIIYQoMenBEkIIIYQoMQlYQgghhBAlJgFLCCGEEKLEJGAJIYQQQpSYBCwhhBBCiBL7P7WuuRhjW01RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.4,\n",
    "             s=housing['population']/100, label='population',\n",
    "            c='median_house_value', cmap=plt.get_cmap('jet'), colorbar=True,\n",
    "            figsize=(10,7), sharex=False,)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687160\n",
       "total_rooms           0.135097\n",
       "housing_median_age    0.114110\n",
       "households            0.064506\n",
       "total_bedrooms        0.047689\n",
       "population           -0.026920\n",
       "longitude            -0.047432\n",
       "latitude             -0.142724\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各属性与房价中位数的相关性\n",
    "corr_matrix['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "17606       <1H OCEAN  \n",
       "18632       <1H OCEAN  \n",
       "14650      NEAR OCEAN  \n",
       "3230           INLAND  \n",
       "3555        <1H OCEAN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDD10A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FE2469E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDF29E48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FE40C438>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDEBF9E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDEC0F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FE3D1588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDD57B70>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDD57BA8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDE706D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDE82C88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDE67278>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDDAC828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDDB0DD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000185FDE0B3C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018580FDF978>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attributes = ['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18581311278>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='median_income', y='median_house_value', alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] = housing['total_rooms'] / housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms'] / housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population'] / housing['households']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.687160\n",
       "rooms_per_household         0.146285\n",
       "total_rooms                 0.135097\n",
       "housing_median_age          0.114110\n",
       "households                  0.064506\n",
       "total_bedrooms              0.047689\n",
       "population_per_household   -0.021985\n",
       "population                 -0.026920\n",
       "longitude                  -0.047432\n",
       "latitude                   -0.142724\n",
       "bedrooms_per_room          -0.259984\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = start_train_set.drop('median_house_value', axis=1)\n",
    "housing_labels = start_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据清理\n",
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Davion\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "imputer = Imputer(strategy='median')\n",
    "housing_num = housing.drop('ocean_proximity', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(x, columns=housing_num.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.19</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3615.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>3.5083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-122.05</td>\n",
       "      <td>37.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4111.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>9.2298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-120.66</td>\n",
       "      <td>35.49</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4422.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>2.8285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-117.25</td>\n",
       "      <td>34.16</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>3.7188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-117.19</td>\n",
       "      <td>34.94</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2034.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>2.1522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>33.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1.9176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-118.07</td>\n",
       "      <td>34.11</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>5.0736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-118.24</td>\n",
       "      <td>33.96</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>1.7778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>33.91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.6546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-122.46</td>\n",
       "      <td>37.79</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.7419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16482</th>\n",
       "      <td>-116.47</td>\n",
       "      <td>33.77</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>4.4107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16483</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.78</td>\n",
       "      <td>20.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.5588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16484</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>39.25</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3127.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>3.9537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16485</th>\n",
       "      <td>-122.03</td>\n",
       "      <td>37.29</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3118.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1147.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>10.3653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16486</th>\n",
       "      <td>-118.17</td>\n",
       "      <td>34.09</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>2.2348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16487</th>\n",
       "      <td>-122.86</td>\n",
       "      <td>40.56</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.7393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16488</th>\n",
       "      <td>-122.48</td>\n",
       "      <td>38.51</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>3.1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16489</th>\n",
       "      <td>-117.17</td>\n",
       "      <td>32.78</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3845.0</td>\n",
       "      <td>1051.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>2.3658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16490</th>\n",
       "      <td>-122.07</td>\n",
       "      <td>37.71</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>5.3015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16491</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.87</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>4188.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>3.1390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16492</th>\n",
       "      <td>-122.21</td>\n",
       "      <td>37.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1702.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1.7188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16493</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.94</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>3.0789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16494</th>\n",
       "      <td>-121.29</td>\n",
       "      <td>38.14</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>3.2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16495</th>\n",
       "      <td>-117.82</td>\n",
       "      <td>33.76</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2774.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>1229.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>6.2944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16496</th>\n",
       "      <td>-119.34</td>\n",
       "      <td>36.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16497</th>\n",
       "      <td>-122.14</td>\n",
       "      <td>38.03</td>\n",
       "      <td>42.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.5795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16498</th>\n",
       "      <td>-120.37</td>\n",
       "      <td>38.23</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2.6942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16499</th>\n",
       "      <td>-118.42</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>5.6454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16500</th>\n",
       "      <td>-117.97</td>\n",
       "      <td>33.88</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>6.0394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16501</th>\n",
       "      <td>-117.67</td>\n",
       "      <td>33.60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>1517.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>6.7921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16502</th>\n",
       "      <td>-117.29</td>\n",
       "      <td>33.08</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>5.7787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16503</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>34.18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2.9728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16504</th>\n",
       "      <td>-117.14</td>\n",
       "      <td>33.81</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4496.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>3.2778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16505</th>\n",
       "      <td>-117.77</td>\n",
       "      <td>34.08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5929.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>2817.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>6.0434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16506</th>\n",
       "      <td>-118.20</td>\n",
       "      <td>33.97</td>\n",
       "      <td>43.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1.8897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16507</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>4.9312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16508</th>\n",
       "      <td>-117.56</td>\n",
       "      <td>33.88</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>2.0682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16509</th>\n",
       "      <td>-116.40</td>\n",
       "      <td>34.09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4855.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>3.2723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16510</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.82</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>4.0625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16511</th>\n",
       "      <td>-122.45</td>\n",
       "      <td>37.77</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3095.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>3.5750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -121.89     37.29                38.0       1568.0           351.0   \n",
       "1        -121.93     37.05                14.0        679.0           108.0   \n",
       "2        -117.20     32.77                31.0       1952.0           471.0   \n",
       "3        -119.61     36.31                25.0       1847.0           371.0   \n",
       "4        -118.59     34.23                17.0       6592.0          1525.0   \n",
       "5        -120.97     37.66                24.0       2930.0           588.0   \n",
       "6        -118.50     34.04                52.0       2233.0           317.0   \n",
       "7        -117.24     34.15                26.0       2041.0           293.0   \n",
       "8        -118.26     33.99                47.0       1865.0           465.0   \n",
       "9        -118.28     34.02                29.0        515.0           229.0   \n",
       "10       -121.31     38.02                24.0       4157.0           951.0   \n",
       "11       -121.62     39.14                41.0       2183.0           559.0   \n",
       "12       -122.69     38.51                18.0       3364.0           501.0   \n",
       "13       -117.06     34.17                21.0       2520.0           582.0   \n",
       "14       -119.46     36.91                12.0       2980.0           495.0   \n",
       "15       -117.96     33.83                30.0       2838.0           649.0   \n",
       "16       -122.41     37.81                25.0       1178.0           545.0   \n",
       "17       -119.02     35.35                42.0       1239.0           251.0   \n",
       "18       -117.72     34.05                 8.0       1841.0           409.0   \n",
       "19       -118.15     34.20                46.0       1505.0           261.0   \n",
       "20       -118.30     34.19                14.0       3615.0           913.0   \n",
       "21       -122.05     37.31                25.0       4111.0           538.0   \n",
       "22       -120.66     35.49                17.0       4422.0           945.0   \n",
       "23       -117.25     34.16                37.0       1709.0           278.0   \n",
       "24       -117.19     34.94                31.0       2034.0           444.0   \n",
       "25       -117.06     33.02                24.0        830.0           190.0   \n",
       "26       -118.07     34.11                41.0       2869.0           563.0   \n",
       "27       -118.24     33.96                44.0       1338.0           366.0   \n",
       "28       -118.28     33.91                41.0        620.0           133.0   \n",
       "29       -122.46     37.79                52.0       2059.0           416.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "16482    -116.47     33.77                26.0       4300.0           767.0   \n",
       "16483    -118.23     33.78                20.0         59.0            24.0   \n",
       "16484    -121.06     39.25                17.0       3127.0           539.0   \n",
       "16485    -122.03     37.29                22.0       3118.0           438.0   \n",
       "16486    -118.17     34.09                33.0       2907.0           797.0   \n",
       "16487    -122.86     40.56                12.0       1350.0           300.0   \n",
       "16488    -122.48     38.51                49.0       1977.0           393.0   \n",
       "16489    -117.17     32.78                17.0       3845.0          1051.0   \n",
       "16490    -122.07     37.71                40.0       1808.0           302.0   \n",
       "16491    -118.01     33.87                25.0       6348.0          1615.0   \n",
       "16492    -122.21     37.78                43.0       1702.0           460.0   \n",
       "16493    -117.23     33.94                 8.0       2405.0           537.0   \n",
       "16494    -121.29     38.14                34.0       2770.0           544.0   \n",
       "16495    -117.82     33.76                33.0       2774.0           428.0   \n",
       "16496    -119.34     36.31                14.0       1635.0           422.0   \n",
       "16497    -122.14     38.03                42.0        118.0            34.0   \n",
       "16498    -120.37     38.23                13.0       4401.0           829.0   \n",
       "16499    -118.42     34.04                52.0       1358.0           272.0   \n",
       "16500    -117.97     33.88                16.0       2003.0           300.0   \n",
       "16501    -117.67     33.60                25.0       3164.0           449.0   \n",
       "16502    -117.29     33.08                18.0       3225.0           515.0   \n",
       "16503    -118.37     34.18                36.0       1608.0           373.0   \n",
       "16504    -117.14     33.81                13.0       4496.0           756.0   \n",
       "16505    -117.77     34.08                27.0       5929.0           932.0   \n",
       "16506    -118.20     33.97                43.0        825.0           212.0   \n",
       "16507    -118.13     34.20                46.0       1271.0           236.0   \n",
       "16508    -117.56     33.88                40.0       1196.0           294.0   \n",
       "16509    -116.40     34.09                 9.0       4855.0           872.0   \n",
       "16510    -118.01     33.82                31.0       1960.0           380.0   \n",
       "16511    -122.45     37.77                52.0       3095.0           682.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "0           710.0       339.0         2.7042  \n",
       "1           306.0       113.0         6.4214  \n",
       "2           936.0       462.0         2.8621  \n",
       "3          1460.0       353.0         1.8839  \n",
       "4          4459.0      1463.0         3.0347  \n",
       "5          1448.0       570.0         3.5395  \n",
       "6           769.0       277.0         8.3839  \n",
       "7           936.0       375.0         6.0000  \n",
       "8          1916.0       438.0         1.8242  \n",
       "9          2690.0       217.0         0.4999  \n",
       "10         2734.0       879.0         2.7981  \n",
       "11         1202.0       506.0         1.6902  \n",
       "12         1442.0       506.0         6.6854  \n",
       "13          416.0       151.0         2.7120  \n",
       "14         1184.0       429.0         3.9141  \n",
       "15         1758.0       593.0         3.3831  \n",
       "16          592.0       441.0         3.6728  \n",
       "17          776.0       272.0         1.9830  \n",
       "18         1243.0       394.0         4.0614  \n",
       "19          857.0       269.0         4.5000  \n",
       "20         1924.0       852.0         3.5083  \n",
       "21         1585.0       568.0         9.2298  \n",
       "22         2307.0       885.0         2.8285  \n",
       "23          744.0       274.0         3.7188  \n",
       "24         1097.0       367.0         2.1522  \n",
       "25          279.0       196.0         1.9176  \n",
       "26         1627.0       533.0         5.0736  \n",
       "27         1765.0       388.0         1.7778  \n",
       "28          642.0       162.0         2.6546  \n",
       "29          999.0       402.0         3.7419  \n",
       "...           ...         ...            ...  \n",
       "16482      1557.0       669.0         4.4107  \n",
       "16483        69.0        23.0         2.5588  \n",
       "16484      1390.0       520.0         3.9537  \n",
       "16485      1147.0       425.0        10.3653  \n",
       "16486      3212.0       793.0         2.2348  \n",
       "16487       423.0       172.0         1.7393  \n",
       "16488       741.0       339.0         3.1312  \n",
       "16489      3102.0       944.0         2.3658  \n",
       "16490       746.0       270.0         5.3015  \n",
       "16491      4188.0      1497.0         3.1390  \n",
       "16492      1227.0       407.0         1.7188  \n",
       "16493      1594.0       517.0         3.0789  \n",
       "16494      1409.0       535.0         3.2338  \n",
       "16495      1229.0       407.0         6.2944  \n",
       "16496       870.0       399.0         2.7000  \n",
       "16497        54.0        30.0         2.5795  \n",
       "16498       924.0       383.0         2.6942  \n",
       "16499       574.0       267.0         5.6454  \n",
       "16500      1172.0       318.0         6.0394  \n",
       "16501      1517.0       453.0         6.7921  \n",
       "16502      1463.0       476.0         5.7787  \n",
       "16503      1217.0       374.0         2.9728  \n",
       "16504      2044.0       695.0         3.2778  \n",
       "16505      2817.0       828.0         6.0434  \n",
       "16506       820.0       184.0         1.8897  \n",
       "16507       573.0       210.0         4.9312  \n",
       "16508      1052.0       258.0         2.0682  \n",
       "16509      2098.0       765.0         3.2723  \n",
       "16510      1356.0       356.0         4.0625  \n",
       "16511      1269.0       639.0         3.5750  \n",
       "\n",
       "[16512 rows x 8 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 文本转向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转化为数字标签\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoded = encoder.fit_transform(housing_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Davion\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转化为独热向量\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接从文本标签转化为独热向量\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 自定义转化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room=True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, x, y=None):\n",
    "        return self\n",
    "    def transform(self, x, y=None):\n",
    "        rooms_per_household = x[:, rooms_ix] / x[:, household_ix]\n",
    "        population_per_household= x[:, population_ix] / x[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = x[:, bedrooms_ix] / x[:, rooms_ix]\n",
    "            return np.c_[x, rooms_per_household, population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[x, rooms_per_household, population_per_household]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., '<1H OCEAN', 4.625368731563422,\n",
       "        2.094395280235988],\n",
       "       [-121.93, 37.05, 14.0, ..., '<1H OCEAN', 6.008849557522124,\n",
       "        2.7079646017699117],\n",
       "       [-117.2, 32.77, 31.0, ..., 'NEAR OCEAN', 4.225108225108225,\n",
       "        2.0259740259740258],\n",
       "       ...,\n",
       "       [-116.4, 34.09, 9.0, ..., 'INLAND', 6.34640522875817,\n",
       "        2.742483660130719],\n",
       "       [-118.01, 33.82, 31.0, ..., '<1H OCEAN', 5.50561797752809,\n",
       "        3.808988764044944],\n",
       "       [-122.45, 37.77, 52.0, ..., 'NEAR BAY', 4.843505477308295,\n",
       "        1.9859154929577465]], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 特征缩放\n",
    "- 如果输入的数值属性具有非常大的比例差异， 往往导致机器学习算法的性能表现不佳， 当然也有极少数特例。 案例中的房屋数据就是这样： 房间总数的范围从6到39320， 而收入中位数的范围是0到15。\n",
    "- 同比例缩放所有属性， 常用的两种方法是： 最小-最大缩放和标准化\n",
    ">- 最小-最大缩放（又叫作归一化） 很简单： 将值重新缩放使其最终范围归于0到1之间。 实现方法是将值减去最小值并除以最大值和最小值的差。\n",
    ">>Scikit-Learn提供了一个名为MinMaxScaler的转换器。 你希望范围不是0～1， 你可以通过调整超参数feature_range进行更改。\n",
    ">- 标准化则完全不一样： 首先减去平均值（所以标准化值的均值总是零） ， 然后除以方差， 从而使得结果的分布具备单位方差。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Davion\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipline = Pipeline([\n",
    "    ('imputer', Imputer(strategy='median')),\n",
    "    ('attr_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler', StandardScaler()),\n",
    "]\n",
    ")\n",
    "housing_num_tr = num_pipline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.31205452,\n",
       "        -0.08649871,  0.15531753],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.21768338,\n",
       "        -0.03353391, -0.83628902],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.46531516,\n",
       "        -0.09240499,  0.4222004 ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.3469342 ,\n",
       "        -0.03055414, -0.52177644],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.02499488,\n",
       "         0.06150916, -0.30340741],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ..., -0.22852947,\n",
       "        -0.09586294,  0.10180567]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_arrtibs = list(housing_num)\n",
    "cat_attribs = ['ocean_proximity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num\", num_pipline, num_arrtibs),\n",
    "        (\"cat\", OneHotEncoder(), cat_attribs),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Davion\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 16)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 选择和训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 培训评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:5]\n",
    "some_label = housing_labels.iloc[:5]\n",
    "some_data_prepared = full_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210644.60459286, 317768.80697211, 210956.43331178,  59218.98886849,\n",
       "       189747.55849879])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.predict(some_data_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[286600.0, 340600.0, 196900.0, 46300.0, 254500.0]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, -0.49323393, -0.44543821,\n",
       "        -0.63621141, -0.42069842, -0.61493744, -0.31205452, -0.08649871,\n",
       "         0.15531753,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , -0.90896655, -1.0369278 ,\n",
       "        -0.99833135, -1.02222705,  1.33645936,  0.21768338, -0.03353391,\n",
       "        -0.83628902,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, -0.31365989, -0.15334458,\n",
       "        -0.43363936, -0.0933178 , -0.5320456 , -0.46531516, -0.09240499,\n",
       "         0.4222004 ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         1.        ],\n",
       "       [-0.01706767,  0.31357576, -0.29052016, -0.36276217, -0.39675594,\n",
       "         0.03604096, -0.38343559, -1.04556555, -0.07966124,  0.08973561,\n",
       "        -0.19645314,  0.        ,  1.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.49247384, -0.65929936, -0.92673619,  1.85619316,  2.41221109,\n",
       "         2.72415407,  2.57097492, -0.44143679, -0.35783383, -0.00419445,\n",
       "         0.2699277 ,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_data_prepared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 测量整个训练集上回归模型的RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848923"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 训练结果貌似不太好，可能是提供的特征无法做出更好的预测，或则是模型不够强大\n",
    "- 想要修正拟合不足，可以通过选择更强大的模型， 或是为算法训练提供更好的特征， 又或者是减少对模型的限制等方法\n",
    "- 这里改用决策树模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 这里一点误差都没有，是出现了模型过拟合现象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用交叉验证来更好的进行评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 交叉验证tree模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                        scoring='neg_mean_squared_error', cv=10)\n",
    "rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_scores(scores):\n",
    "    print('Scores:', scores)\n",
    "    print('Mean:', scores.mean())\n",
    "    print('Standard deviation:', scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [69505.92895287 65200.26825058 70356.39227113 68504.00573319\n",
      " 71827.39431694 74799.20498524 72851.80762878 71628.04045096\n",
      " 75934.94493677 70957.94824189]\n",
      "Mean: 71156.59357683339\n",
      "Standard deviation: 2925.1624830362653\n"
     ]
    }
   ],
   "source": [
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 交叉验证线性回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                            scoring='neg_mean_squared_error', cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66782.73843989 66960.118071   70347.95244419 74739.57052552\n",
      " 68031.13388938 71193.84183426 64969.63056405 68281.61137997\n",
      " 71552.91566558 67665.10082067]\n",
      "Mean: 69052.46136345083\n",
      "Standard deviation: 2731.674001798349\n"
     ]
    }
   ],
   "source": [
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forset_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forset_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21933.31414779769"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forset_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_scores = cross_val_score(forset_reg, housing_prepared, housing_labels,\n",
    "                               scoring='neg_mean_squared_error', cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51646.44545909 48940.60114882 53050.86323649 54408.98730149\n",
      " 50922.14870785 56482.50703987 51864.52025526 49760.85037653\n",
      " 55434.21627933 53326.10093303]\n",
      "Mean: 52583.72407377466\n",
      "Standard deviation: 2298.353351147122\n"
     ]
    }
   ],
   "source": [
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 训练模型保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lin_reg.pkl']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(forset_reg, 'forest_reg.pkl')\n",
    "joblib.dump(tree_reg, 'tree_reg.pkl')\n",
    "joblib.dump(lin_reg, 'lin_reg.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 网格搜索自动调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {'n_estimators': [3,10,30], 'max_features': [2,4,6,8]},\n",
    "    {'bootsrtap': [False], 'n_estimators': [3,10], 'max_features': [2,3,4]},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_reg = joblib.load('forest_reg.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestRegressor(bootstrap=True, criterion='mse',\n",
       "                                             max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators='warn', n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    # try 12 (3×4) combinations of hyperparameters\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    # then try 6 (2×3) combinations with bootstrap set as False\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = [{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},]\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features=8, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=30,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvres = grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63669.05791727153 {'max_features': 2, 'n_estimators': 3}\n",
      "55627.16171305252 {'max_features': 2, 'n_estimators': 10}\n",
      "53384.57867637289 {'max_features': 2, 'n_estimators': 30}\n",
      "60965.99185930139 {'max_features': 4, 'n_estimators': 3}\n",
      "52740.98248528835 {'max_features': 4, 'n_estimators': 10}\n",
      "50377.344409590376 {'max_features': 4, 'n_estimators': 30}\n",
      "58663.84733372485 {'max_features': 6, 'n_estimators': 3}\n",
      "52006.15355973719 {'max_features': 6, 'n_estimators': 10}\n",
      "50146.465964159885 {'max_features': 6, 'n_estimators': 30}\n",
      "57869.25504027614 {'max_features': 8, 'n_estimators': 3}\n",
      "51711.09443660957 {'max_features': 8, 'n_estimators': 10}\n",
      "49682.25345942335 {'max_features': 8, 'n_estimators': 30}\n",
      "62895.088889905004 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54658.14484390074 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "59470.399594730654 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52725.01091081235 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "57490.612956065226 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51009.51445842374 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "for mean_score, params in zip(cvres['mean_test_score'], cvres['params']):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_bootstrap</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.068224</td>\n",
       "      <td>0.005020</td>\n",
       "      <td>0.003591</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 3}</td>\n",
       "      <td>-3.837622e+09</td>\n",
       "      <td>-4.147108e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.053749e+09</td>\n",
       "      <td>1.519609e+08</td>\n",
       "      <td>18</td>\n",
       "      <td>-1.064113e+09</td>\n",
       "      <td>-1.105142e+09</td>\n",
       "      <td>-1.116550e+09</td>\n",
       "      <td>-1.112342e+09</td>\n",
       "      <td>-1.129650e+09</td>\n",
       "      <td>-1.105559e+09</td>\n",
       "      <td>2.220402e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.209250</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>0.008978</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 10}</td>\n",
       "      <td>-3.047771e+09</td>\n",
       "      <td>-3.254861e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.094381e+09</td>\n",
       "      <td>1.327046e+08</td>\n",
       "      <td>11</td>\n",
       "      <td>-5.927175e+08</td>\n",
       "      <td>-5.870952e+08</td>\n",
       "      <td>-5.776964e+08</td>\n",
       "      <td>-5.716332e+08</td>\n",
       "      <td>-5.802501e+08</td>\n",
       "      <td>-5.818785e+08</td>\n",
       "      <td>7.345821e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.629313</td>\n",
       "      <td>0.007903</td>\n",
       "      <td>0.024934</td>\n",
       "      <td>0.001091</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 2, 'n_estimators': 30}</td>\n",
       "      <td>-2.689185e+09</td>\n",
       "      <td>-3.021086e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.849913e+09</td>\n",
       "      <td>1.626879e+08</td>\n",
       "      <td>9</td>\n",
       "      <td>-4.381089e+08</td>\n",
       "      <td>-4.391272e+08</td>\n",
       "      <td>-4.371702e+08</td>\n",
       "      <td>-4.376955e+08</td>\n",
       "      <td>-4.452654e+08</td>\n",
       "      <td>-4.394734e+08</td>\n",
       "      <td>2.966320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.101137</td>\n",
       "      <td>0.002050</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 3}</td>\n",
       "      <td>-3.730181e+09</td>\n",
       "      <td>-3.786886e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.716852e+09</td>\n",
       "      <td>1.631421e+08</td>\n",
       "      <td>16</td>\n",
       "      <td>-9.865163e+08</td>\n",
       "      <td>-1.012565e+09</td>\n",
       "      <td>-9.169425e+08</td>\n",
       "      <td>-1.037400e+09</td>\n",
       "      <td>-9.707739e+08</td>\n",
       "      <td>-9.848396e+08</td>\n",
       "      <td>4.084607e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.329522</td>\n",
       "      <td>0.004752</td>\n",
       "      <td>0.008975</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 10}</td>\n",
       "      <td>-2.666283e+09</td>\n",
       "      <td>-2.784511e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.781611e+09</td>\n",
       "      <td>1.268562e+08</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.097115e+08</td>\n",
       "      <td>-5.162820e+08</td>\n",
       "      <td>-4.962893e+08</td>\n",
       "      <td>-5.436192e+08</td>\n",
       "      <td>-5.160297e+08</td>\n",
       "      <td>-5.163863e+08</td>\n",
       "      <td>1.542862e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.982971</td>\n",
       "      <td>0.009105</td>\n",
       "      <td>0.024927</td>\n",
       "      <td>0.001530</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 4, 'n_estimators': 30}</td>\n",
       "      <td>-2.387153e+09</td>\n",
       "      <td>-2.588448e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.537877e+09</td>\n",
       "      <td>1.214603e+08</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.838835e+08</td>\n",
       "      <td>-3.880268e+08</td>\n",
       "      <td>-3.790867e+08</td>\n",
       "      <td>-4.040957e+08</td>\n",
       "      <td>-3.845520e+08</td>\n",
       "      <td>-3.879289e+08</td>\n",
       "      <td>8.571233e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.135852</td>\n",
       "      <td>0.005252</td>\n",
       "      <td>0.002992</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 3}</td>\n",
       "      <td>-3.119657e+09</td>\n",
       "      <td>-3.586319e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.441447e+09</td>\n",
       "      <td>1.893141e+08</td>\n",
       "      <td>14</td>\n",
       "      <td>-9.245343e+08</td>\n",
       "      <td>-8.886939e+08</td>\n",
       "      <td>-9.353135e+08</td>\n",
       "      <td>-9.009801e+08</td>\n",
       "      <td>-8.624664e+08</td>\n",
       "      <td>-9.023976e+08</td>\n",
       "      <td>2.591445e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.443221</td>\n",
       "      <td>0.003062</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.001164</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 10}</td>\n",
       "      <td>-2.549663e+09</td>\n",
       "      <td>-2.782039e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.704640e+09</td>\n",
       "      <td>1.471542e+08</td>\n",
       "      <td>6</td>\n",
       "      <td>-4.980344e+08</td>\n",
       "      <td>-5.045869e+08</td>\n",
       "      <td>-4.994664e+08</td>\n",
       "      <td>-4.990325e+08</td>\n",
       "      <td>-5.055542e+08</td>\n",
       "      <td>-5.013349e+08</td>\n",
       "      <td>3.100456e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.345592</td>\n",
       "      <td>0.005771</td>\n",
       "      <td>0.025142</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 6, 'n_estimators': 30}</td>\n",
       "      <td>-2.370010e+09</td>\n",
       "      <td>-2.583638e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.514668e+09</td>\n",
       "      <td>1.285063e+08</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.838538e+08</td>\n",
       "      <td>-3.804711e+08</td>\n",
       "      <td>-3.805218e+08</td>\n",
       "      <td>-3.856095e+08</td>\n",
       "      <td>-3.901917e+08</td>\n",
       "      <td>-3.841296e+08</td>\n",
       "      <td>3.617057e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.170952</td>\n",
       "      <td>0.003372</td>\n",
       "      <td>0.003192</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 3}</td>\n",
       "      <td>-3.353504e+09</td>\n",
       "      <td>-3.348552e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.348851e+09</td>\n",
       "      <td>1.241864e+08</td>\n",
       "      <td>13</td>\n",
       "      <td>-9.228123e+08</td>\n",
       "      <td>-8.553031e+08</td>\n",
       "      <td>-8.603321e+08</td>\n",
       "      <td>-8.881964e+08</td>\n",
       "      <td>-9.151287e+08</td>\n",
       "      <td>-8.883545e+08</td>\n",
       "      <td>2.750227e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.564901</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.008766</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 10}</td>\n",
       "      <td>-2.571970e+09</td>\n",
       "      <td>-2.718994e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.674037e+09</td>\n",
       "      <td>1.392720e+08</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.932416e+08</td>\n",
       "      <td>-4.815238e+08</td>\n",
       "      <td>-4.730979e+08</td>\n",
       "      <td>-5.155367e+08</td>\n",
       "      <td>-4.985555e+08</td>\n",
       "      <td>-4.923911e+08</td>\n",
       "      <td>1.459294e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.706615</td>\n",
       "      <td>0.014689</td>\n",
       "      <td>0.025947</td>\n",
       "      <td>0.001563</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'max_features': 8, 'n_estimators': 30}</td>\n",
       "      <td>-2.357390e+09</td>\n",
       "      <td>-2.546640e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.468326e+09</td>\n",
       "      <td>1.091647e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.841658e+08</td>\n",
       "      <td>-3.744500e+08</td>\n",
       "      <td>-3.773239e+08</td>\n",
       "      <td>-3.882250e+08</td>\n",
       "      <td>-3.810005e+08</td>\n",
       "      <td>-3.810330e+08</td>\n",
       "      <td>4.871017e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.100133</td>\n",
       "      <td>0.001838</td>\n",
       "      <td>0.003998</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
       "      <td>-3.785816e+09</td>\n",
       "      <td>-4.166012e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.955792e+09</td>\n",
       "      <td>1.900966e+08</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.331914</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>0.010565</td>\n",
       "      <td>0.001181</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 2, 'n_est...</td>\n",
       "      <td>-2.810721e+09</td>\n",
       "      <td>-3.107789e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.987513e+09</td>\n",
       "      <td>1.539231e+08</td>\n",
       "      <td>10</td>\n",
       "      <td>-6.056477e-02</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-2.967449e+00</td>\n",
       "      <td>-6.056027e-01</td>\n",
       "      <td>1.181156e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.130649</td>\n",
       "      <td>0.003025</td>\n",
       "      <td>0.003391</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
       "      <td>-3.618324e+09</td>\n",
       "      <td>-3.441527e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.536728e+09</td>\n",
       "      <td>7.795196e+07</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-6.072840e+01</td>\n",
       "      <td>-1.214568e+01</td>\n",
       "      <td>2.429136e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.428061</td>\n",
       "      <td>0.005019</td>\n",
       "      <td>0.010174</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 3, 'n_est...</td>\n",
       "      <td>-2.757999e+09</td>\n",
       "      <td>-2.851737e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.779927e+09</td>\n",
       "      <td>6.286611e+07</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.089484e+01</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-5.465556e+00</td>\n",
       "      <td>-5.272080e+00</td>\n",
       "      <td>8.093117e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.165367</td>\n",
       "      <td>0.004692</td>\n",
       "      <td>0.003391</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
       "      <td>-3.134040e+09</td>\n",
       "      <td>-3.559375e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.305171e+09</td>\n",
       "      <td>1.879203e+08</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.528789</td>\n",
       "      <td>0.005255</td>\n",
       "      <td>0.009773</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>{'bootstrap': False, 'max_features': 4, 'n_est...</td>\n",
       "      <td>-2.525578e+09</td>\n",
       "      <td>-2.710011e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.601971e+09</td>\n",
       "      <td>1.088031e+08</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.514119e-02</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-3.028238e-03</td>\n",
       "      <td>6.056477e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.068224      0.005020         0.003591        0.000489   \n",
       "1        0.209250      0.002032         0.008978        0.000002   \n",
       "2        0.629313      0.007903         0.024934        0.001091   \n",
       "3        0.101137      0.002050         0.002991        0.000002   \n",
       "4        0.329522      0.004752         0.008975        0.000001   \n",
       "5        0.982971      0.009105         0.024927        0.001530   \n",
       "6        0.135852      0.005252         0.002992        0.000003   \n",
       "7        0.443221      0.003062         0.008775        0.001164   \n",
       "8        1.345592      0.005771         0.025142        0.000415   \n",
       "9        0.170952      0.003372         0.003192        0.000400   \n",
       "10       0.564901      0.002925         0.008766        0.000396   \n",
       "11       1.706615      0.014689         0.025947        0.001563   \n",
       "12       0.100133      0.001838         0.003998        0.000644   \n",
       "13       0.331914      0.002633         0.010565        0.001181   \n",
       "14       0.130649      0.003025         0.003391        0.000489   \n",
       "15       0.428061      0.005019         0.010174        0.000398   \n",
       "16       0.165367      0.004692         0.003391        0.000489   \n",
       "17       0.528789      0.005255         0.009773        0.000398   \n",
       "\n",
       "   param_max_features param_n_estimators param_bootstrap  \\\n",
       "0                   2                  3             NaN   \n",
       "1                   2                 10             NaN   \n",
       "2                   2                 30             NaN   \n",
       "3                   4                  3             NaN   \n",
       "4                   4                 10             NaN   \n",
       "5                   4                 30             NaN   \n",
       "6                   6                  3             NaN   \n",
       "7                   6                 10             NaN   \n",
       "8                   6                 30             NaN   \n",
       "9                   8                  3             NaN   \n",
       "10                  8                 10             NaN   \n",
       "11                  8                 30             NaN   \n",
       "12                  2                  3           False   \n",
       "13                  2                 10           False   \n",
       "14                  3                  3           False   \n",
       "15                  3                 10           False   \n",
       "16                  4                  3           False   \n",
       "17                  4                 10           False   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0              {'max_features': 2, 'n_estimators': 3}      -3.837622e+09   \n",
       "1             {'max_features': 2, 'n_estimators': 10}      -3.047771e+09   \n",
       "2             {'max_features': 2, 'n_estimators': 30}      -2.689185e+09   \n",
       "3              {'max_features': 4, 'n_estimators': 3}      -3.730181e+09   \n",
       "4             {'max_features': 4, 'n_estimators': 10}      -2.666283e+09   \n",
       "5             {'max_features': 4, 'n_estimators': 30}      -2.387153e+09   \n",
       "6              {'max_features': 6, 'n_estimators': 3}      -3.119657e+09   \n",
       "7             {'max_features': 6, 'n_estimators': 10}      -2.549663e+09   \n",
       "8             {'max_features': 6, 'n_estimators': 30}      -2.370010e+09   \n",
       "9              {'max_features': 8, 'n_estimators': 3}      -3.353504e+09   \n",
       "10            {'max_features': 8, 'n_estimators': 10}      -2.571970e+09   \n",
       "11            {'max_features': 8, 'n_estimators': 30}      -2.357390e+09   \n",
       "12  {'bootstrap': False, 'max_features': 2, 'n_est...      -3.785816e+09   \n",
       "13  {'bootstrap': False, 'max_features': 2, 'n_est...      -2.810721e+09   \n",
       "14  {'bootstrap': False, 'max_features': 3, 'n_est...      -3.618324e+09   \n",
       "15  {'bootstrap': False, 'max_features': 3, 'n_est...      -2.757999e+09   \n",
       "16  {'bootstrap': False, 'max_features': 4, 'n_est...      -3.134040e+09   \n",
       "17  {'bootstrap': False, 'max_features': 4, 'n_est...      -2.525578e+09   \n",
       "\n",
       "    split1_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0       -4.147108e+09  ...    -4.053749e+09    1.519609e+08               18   \n",
       "1       -3.254861e+09  ...    -3.094381e+09    1.327046e+08               11   \n",
       "2       -3.021086e+09  ...    -2.849913e+09    1.626879e+08                9   \n",
       "3       -3.786886e+09  ...    -3.716852e+09    1.631421e+08               16   \n",
       "4       -2.784511e+09  ...    -2.781611e+09    1.268562e+08                8   \n",
       "5       -2.588448e+09  ...    -2.537877e+09    1.214603e+08                3   \n",
       "6       -3.586319e+09  ...    -3.441447e+09    1.893141e+08               14   \n",
       "7       -2.782039e+09  ...    -2.704640e+09    1.471542e+08                6   \n",
       "8       -2.583638e+09  ...    -2.514668e+09    1.285063e+08                2   \n",
       "9       -3.348552e+09  ...    -3.348851e+09    1.241864e+08               13   \n",
       "10      -2.718994e+09  ...    -2.674037e+09    1.392720e+08                5   \n",
       "11      -2.546640e+09  ...    -2.468326e+09    1.091647e+08                1   \n",
       "12      -4.166012e+09  ...    -3.955792e+09    1.900966e+08               17   \n",
       "13      -3.107789e+09  ...    -2.987513e+09    1.539231e+08               10   \n",
       "14      -3.441527e+09  ...    -3.536728e+09    7.795196e+07               15   \n",
       "15      -2.851737e+09  ...    -2.779927e+09    6.286611e+07                7   \n",
       "16      -3.559375e+09  ...    -3.305171e+09    1.879203e+08               12   \n",
       "17      -2.710011e+09  ...    -2.601971e+09    1.088031e+08                4   \n",
       "\n",
       "    split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0        -1.064113e+09       -1.105142e+09       -1.116550e+09   \n",
       "1        -5.927175e+08       -5.870952e+08       -5.776964e+08   \n",
       "2        -4.381089e+08       -4.391272e+08       -4.371702e+08   \n",
       "3        -9.865163e+08       -1.012565e+09       -9.169425e+08   \n",
       "4        -5.097115e+08       -5.162820e+08       -4.962893e+08   \n",
       "5        -3.838835e+08       -3.880268e+08       -3.790867e+08   \n",
       "6        -9.245343e+08       -8.886939e+08       -9.353135e+08   \n",
       "7        -4.980344e+08       -5.045869e+08       -4.994664e+08   \n",
       "8        -3.838538e+08       -3.804711e+08       -3.805218e+08   \n",
       "9        -9.228123e+08       -8.553031e+08       -8.603321e+08   \n",
       "10       -4.932416e+08       -4.815238e+08       -4.730979e+08   \n",
       "11       -3.841658e+08       -3.744500e+08       -3.773239e+08   \n",
       "12       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "13       -6.056477e-02       -0.000000e+00       -0.000000e+00   \n",
       "14       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "15       -2.089484e+01       -0.000000e+00       -0.000000e+00   \n",
       "16       -0.000000e+00       -0.000000e+00       -0.000000e+00   \n",
       "17       -0.000000e+00       -1.514119e-02       -0.000000e+00   \n",
       "\n",
       "    split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0        -1.112342e+09       -1.129650e+09     -1.105559e+09     2.220402e+07  \n",
       "1        -5.716332e+08       -5.802501e+08     -5.818785e+08     7.345821e+06  \n",
       "2        -4.376955e+08       -4.452654e+08     -4.394734e+08     2.966320e+06  \n",
       "3        -1.037400e+09       -9.707739e+08     -9.848396e+08     4.084607e+07  \n",
       "4        -5.436192e+08       -5.160297e+08     -5.163863e+08     1.542862e+07  \n",
       "5        -4.040957e+08       -3.845520e+08     -3.879289e+08     8.571233e+06  \n",
       "6        -9.009801e+08       -8.624664e+08     -9.023976e+08     2.591445e+07  \n",
       "7        -4.990325e+08       -5.055542e+08     -5.013349e+08     3.100456e+06  \n",
       "8        -3.856095e+08       -3.901917e+08     -3.841296e+08     3.617057e+06  \n",
       "9        -8.881964e+08       -9.151287e+08     -8.883545e+08     2.750227e+07  \n",
       "10       -5.155367e+08       -4.985555e+08     -4.923911e+08     1.459294e+07  \n",
       "11       -3.882250e+08       -3.810005e+08     -3.810330e+08     4.871017e+06  \n",
       "12       -0.000000e+00       -0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "13       -0.000000e+00       -2.967449e+00     -6.056027e-01     1.181156e+00  \n",
       "14       -0.000000e+00       -6.072840e+01     -1.214568e+01     2.429136e+01  \n",
       "15       -0.000000e+00       -5.465556e+00     -5.272080e+00     8.093117e+00  \n",
       "16       -0.000000e+00       -0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "17       -0.000000e+00       -0.000000e+00     -3.028238e-03     6.056477e-03  \n",
       "\n",
       "[18 rows x 23 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(cvres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators='warn',\n",
       "                                                   n_jobs=None, oob_score=False,\n",
       "                                                   random_sta...\n",
       "                                                   warm_start=False),\n",
       "                   iid='warn', n_iter=10, n_jobs=None,\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000185922FEC18>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018590766FD0>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 7, 'n_estimators': 180}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_cv_result = rnd_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49150.657232934034 {'max_features': 7, 'n_estimators': 180}\n",
      "51389.85295710133 {'max_features': 5, 'n_estimators': 15}\n",
      "50796.12045980556 {'max_features': 3, 'n_estimators': 72}\n",
      "50835.09932039744 {'max_features': 5, 'n_estimators': 21}\n",
      "49280.90117886215 {'max_features': 7, 'n_estimators': 122}\n",
      "50774.86679035961 {'max_features': 3, 'n_estimators': 75}\n",
      "50682.75001237282 {'max_features': 3, 'n_estimators': 88}\n",
      "49608.94061293652 {'max_features': 5, 'n_estimators': 100}\n",
      "50473.57642831875 {'max_features': 3, 'n_estimators': 150}\n",
      "64429.763804893395 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "for mean_score, params in zip(rnd_cv_result['mean_test_score'], rnd_cv_result['params']):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.514352</td>\n",
       "      <td>0.141093</td>\n",
       "      <td>0.152785</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>7</td>\n",
       "      <td>180</td>\n",
       "      <td>{'max_features': 7, 'n_estimators': 180}</td>\n",
       "      <td>-2.274768e+09</td>\n",
       "      <td>-2.475113e+09</td>\n",
       "      <td>-2.558241e+09</td>\n",
       "      <td>-2.261587e+09</td>\n",
       "      <td>-2.509251e+09</td>\n",
       "      <td>-2.415787e+09</td>\n",
       "      <td>1.234586e+08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.585033</td>\n",
       "      <td>0.004747</td>\n",
       "      <td>0.012766</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>{'max_features': 5, 'n_estimators': 15}</td>\n",
       "      <td>-2.493183e+09</td>\n",
       "      <td>-2.727145e+09</td>\n",
       "      <td>-2.747621e+09</td>\n",
       "      <td>-2.505491e+09</td>\n",
       "      <td>-2.731163e+09</td>\n",
       "      <td>-2.640917e+09</td>\n",
       "      <td>1.158718e+08</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.006031</td>\n",
       "      <td>0.092506</td>\n",
       "      <td>0.060439</td>\n",
       "      <td>0.003644</td>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "      <td>{'max_features': 3, 'n_estimators': 72}</td>\n",
       "      <td>-2.432244e+09</td>\n",
       "      <td>-2.669938e+09</td>\n",
       "      <td>-2.663783e+09</td>\n",
       "      <td>-2.416819e+09</td>\n",
       "      <td>-2.718463e+09</td>\n",
       "      <td>-2.580246e+09</td>\n",
       "      <td>1.286384e+08</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.892388</td>\n",
       "      <td>0.022359</td>\n",
       "      <td>0.021144</td>\n",
       "      <td>0.002042</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>{'max_features': 5, 'n_estimators': 21}</td>\n",
       "      <td>-2.425443e+09</td>\n",
       "      <td>-2.685426e+09</td>\n",
       "      <td>-2.684721e+09</td>\n",
       "      <td>-2.445756e+09</td>\n",
       "      <td>-2.679708e+09</td>\n",
       "      <td>-2.584207e+09</td>\n",
       "      <td>1.215280e+08</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.738563</td>\n",
       "      <td>0.132626</td>\n",
       "      <td>0.113101</td>\n",
       "      <td>0.014198</td>\n",
       "      <td>7</td>\n",
       "      <td>122</td>\n",
       "      <td>{'max_features': 7, 'n_estimators': 122}</td>\n",
       "      <td>-2.295829e+09</td>\n",
       "      <td>-2.490105e+09</td>\n",
       "      <td>-2.567315e+09</td>\n",
       "      <td>-2.275918e+09</td>\n",
       "      <td>-2.513890e+09</td>\n",
       "      <td>-2.428607e+09</td>\n",
       "      <td>1.193625e+08</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.052117</td>\n",
       "      <td>0.045126</td>\n",
       "      <td>0.063430</td>\n",
       "      <td>0.004911</td>\n",
       "      <td>3</td>\n",
       "      <td>75</td>\n",
       "      <td>{'max_features': 3, 'n_estimators': 75}</td>\n",
       "      <td>-2.426584e+09</td>\n",
       "      <td>-2.662806e+09</td>\n",
       "      <td>-2.668221e+09</td>\n",
       "      <td>-2.416089e+09</td>\n",
       "      <td>-2.716755e+09</td>\n",
       "      <td>-2.578087e+09</td>\n",
       "      <td>1.294031e+08</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.379228</td>\n",
       "      <td>0.038338</td>\n",
       "      <td>0.071213</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>3</td>\n",
       "      <td>88</td>\n",
       "      <td>{'max_features': 3, 'n_estimators': 88}</td>\n",
       "      <td>-2.421750e+09</td>\n",
       "      <td>-2.650683e+09</td>\n",
       "      <td>-2.663952e+09</td>\n",
       "      <td>-2.405542e+09</td>\n",
       "      <td>-2.701799e+09</td>\n",
       "      <td>-2.568741e+09</td>\n",
       "      <td>1.278458e+08</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.104614</td>\n",
       "      <td>0.201093</td>\n",
       "      <td>0.087959</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_features': 5, 'n_estimators': 100}</td>\n",
       "      <td>-2.293212e+09</td>\n",
       "      <td>-2.538021e+09</td>\n",
       "      <td>-2.595201e+09</td>\n",
       "      <td>-2.312129e+09</td>\n",
       "      <td>-2.566701e+09</td>\n",
       "      <td>-2.461047e+09</td>\n",
       "      <td>1.307136e+08</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.205345</td>\n",
       "      <td>0.119737</td>\n",
       "      <td>0.130252</td>\n",
       "      <td>0.006634</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>{'max_features': 3, 'n_estimators': 150}</td>\n",
       "      <td>-2.397906e+09</td>\n",
       "      <td>-2.625800e+09</td>\n",
       "      <td>-2.656686e+09</td>\n",
       "      <td>-2.381674e+09</td>\n",
       "      <td>-2.675865e+09</td>\n",
       "      <td>-2.547582e+09</td>\n",
       "      <td>1.299270e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.083191</td>\n",
       "      <td>0.002854</td>\n",
       "      <td>0.002380</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_features': 5, 'n_estimators': 2}</td>\n",
       "      <td>-3.977848e+09</td>\n",
       "      <td>-4.159390e+09</td>\n",
       "      <td>-4.040393e+09</td>\n",
       "      <td>-4.162441e+09</td>\n",
       "      <td>-4.415951e+09</td>\n",
       "      <td>-4.151194e+09</td>\n",
       "      <td>1.500742e+08</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       9.514352      0.141093         0.152785        0.006380   \n",
       "1       0.585033      0.004747         0.012766        0.000399   \n",
       "2       2.006031      0.092506         0.060439        0.003644   \n",
       "3       0.892388      0.022359         0.021144        0.002042   \n",
       "4       6.738563      0.132626         0.113101        0.014198   \n",
       "5       2.052117      0.045126         0.063430        0.004911   \n",
       "6       2.379228      0.038338         0.071213        0.000798   \n",
       "7       4.104614      0.201093         0.087959        0.006245   \n",
       "8       4.205345      0.119737         0.130252        0.006634   \n",
       "9       0.083191      0.002854         0.002380        0.000487   \n",
       "\n",
       "  param_max_features param_n_estimators  \\\n",
       "0                  7                180   \n",
       "1                  5                 15   \n",
       "2                  3                 72   \n",
       "3                  5                 21   \n",
       "4                  7                122   \n",
       "5                  3                 75   \n",
       "6                  3                 88   \n",
       "7                  5                100   \n",
       "8                  3                150   \n",
       "9                  5                  2   \n",
       "\n",
       "                                     params  split0_test_score  \\\n",
       "0  {'max_features': 7, 'n_estimators': 180}      -2.274768e+09   \n",
       "1   {'max_features': 5, 'n_estimators': 15}      -2.493183e+09   \n",
       "2   {'max_features': 3, 'n_estimators': 72}      -2.432244e+09   \n",
       "3   {'max_features': 5, 'n_estimators': 21}      -2.425443e+09   \n",
       "4  {'max_features': 7, 'n_estimators': 122}      -2.295829e+09   \n",
       "5   {'max_features': 3, 'n_estimators': 75}      -2.426584e+09   \n",
       "6   {'max_features': 3, 'n_estimators': 88}      -2.421750e+09   \n",
       "7  {'max_features': 5, 'n_estimators': 100}      -2.293212e+09   \n",
       "8  {'max_features': 3, 'n_estimators': 150}      -2.397906e+09   \n",
       "9    {'max_features': 5, 'n_estimators': 2}      -3.977848e+09   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0      -2.475113e+09      -2.558241e+09      -2.261587e+09      -2.509251e+09   \n",
       "1      -2.727145e+09      -2.747621e+09      -2.505491e+09      -2.731163e+09   \n",
       "2      -2.669938e+09      -2.663783e+09      -2.416819e+09      -2.718463e+09   \n",
       "3      -2.685426e+09      -2.684721e+09      -2.445756e+09      -2.679708e+09   \n",
       "4      -2.490105e+09      -2.567315e+09      -2.275918e+09      -2.513890e+09   \n",
       "5      -2.662806e+09      -2.668221e+09      -2.416089e+09      -2.716755e+09   \n",
       "6      -2.650683e+09      -2.663952e+09      -2.405542e+09      -2.701799e+09   \n",
       "7      -2.538021e+09      -2.595201e+09      -2.312129e+09      -2.566701e+09   \n",
       "8      -2.625800e+09      -2.656686e+09      -2.381674e+09      -2.675865e+09   \n",
       "9      -4.159390e+09      -4.040393e+09      -4.162441e+09      -4.415951e+09   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0    -2.415787e+09    1.234586e+08                1  \n",
       "1    -2.640917e+09    1.158718e+08                9  \n",
       "2    -2.580246e+09    1.286384e+08                7  \n",
       "3    -2.584207e+09    1.215280e+08                8  \n",
       "4    -2.428607e+09    1.193625e+08                2  \n",
       "5    -2.578087e+09    1.294031e+08                6  \n",
       "6    -2.568741e+09    1.278458e+08                5  \n",
       "7    -2.461047e+09    1.307136e+08                3  \n",
       "8    -2.547582e+09    1.299270e+08                4  \n",
       "9    -4.151194e+09    1.500742e+08               10  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(rnd_cv_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分析最佳模型及其错误"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.33442355e-02, 6.29090705e-02, 4.11437985e-02, 1.46726854e-02,\n",
       "       1.41064835e-02, 1.48742809e-02, 1.42575993e-02, 3.66158981e-01,\n",
       "       5.64191792e-02, 1.08792957e-01, 5.33510773e-02, 1.03114883e-02,\n",
       "       1.64780994e-01, 6.02803867e-05, 1.96041560e-03, 2.85647464e-03])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_attribs = ['rooms_per_hhold', 'pop_per_hhold', 'bedrooms_per)room']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_one_hot_attribs = list(encoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = num_arrtibs + extra_attribs + cat_one_hot_attribs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 查看每个属性的相对重要程度（权重）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.36615898061813423, 'median_income'),\n",
       " (0.16478099356159054, 'INLAND'),\n",
       " (0.10879295677551575, 'pop_per_hhold'),\n",
       " (0.07334423551601243, 'longitude'),\n",
       " (0.06290907048262032, 'latitude'),\n",
       " (0.056419179181954014, 'rooms_per_hhold'),\n",
       " (0.053351077347675815, 'bedrooms_per)room'),\n",
       " (0.04114379847872964, 'housing_median_age'),\n",
       " (0.014874280890402769, 'population'),\n",
       " (0.014672685420543239, 'total_rooms'),\n",
       " (0.014257599323407808, 'households'),\n",
       " (0.014106483453584104, 'total_bedrooms'),\n",
       " (0.010311488326303788, '<1H OCEAN'),\n",
       " (0.0028564746373201584, 'NEAR OCEAN'),\n",
       " (0.0019604155994780706, 'NEAR BAY'),\n",
       " (6.0280386727366e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = start_test_set.drop('median_house_value', axis=1)\n",
    "y_test = start_test_set['median_house_value'].copy()\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "final_predictions = final_model.predict(X_test_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2278174560.2938914\n",
      "47730.22690385927\n"
     ]
    }
   ],
   "source": [
    "print(final_mse)\n",
    "print(final_rmse)"
   ]
  }
 ],
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